Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Unsupervised Learning, Image Reconstruction, Dynamic MRI

Motivation: Current unsupervised dynamic-MRI reconstruction algorithms based on DIP uses very low-dimensional latent variables and a single generator for direct non-linear mapping, which may limit the performance.

Goal(s): To propose a new model and algorithm for unsupervised dynamic MRI reconstruction.

Approach: We propose a novel Graph-Image-Prior(GIP) model, which uses branched CNN generators to recover the image structure, and use a Graph-Neural-Network(GNN) to discover the best spatio-temporal manifold. Besides, we devise an ADMM algorithm to alternately optimize the dynamic image and network.

Results: The proposed method achieves the state-of-art performance even compared with supervised deep-learning methods, without the need for any fully-sampled data.

Impact: The proposed Graph-Image-Prior(GIP) scheme is a new unsupervised image reconstruction model, which has a significant value for further research. Besides, GIP is promising to be used in other multi-frame MRI reconstruction applications where fully-sampled data is scarce or unavailable.

Keywords: Neurofluids, Neurofluids, choroid plexus, Blood-CSF barrier, relaxation exchange

Motivation: Scarcity of non-invasive imaging techniques of choroid plexus function hindered our knowledge of the blood-cerebrospinal fluid barrier (BCSFB).

Goal(s): We aimed to measure the trans-barrier water exchange rate in choroid plexus.

Approach: We developed a new imaging method and contrast mechanism, named relaxation-exchange imaging (REXI), and validated its feasibility on both phantoms and rats.

Results: REXI successfully captured the changes in proton exchange rate of urea-water phantoms at varying pH. In-vivo experiments on rats showed the potential of REXI to measure the trans-barrier water exchange in choroid plexus.

Impact: Given the emerging importance of neurofluids and choroid plexus, our novel MRI method REXI provides a way to measure the trans-barrier water exchange in CP and a potential imaging tool to evaluate CP function in future studies.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: SENSE-based reconstruction is challenging for clinical imaging when rotating the RRFCA into multiple positions; therefore, a novel calibration-free GRAPPA-based method was developed.

Goal(s): To effectively reconstruct k-space data acquired from the RRFCA, enhancing image quality compared to a conventional stationary array without a scan time penalty.  

Approach: Conventional GRAPPA was extended by uncovering a subset of the radial grid to cope with the rotation of the RRFCA. Numerical and human brain images were used for validation.

Results: Image quality was improved using the proposed method. Up to 58% reduction in RMSE and 2.5% increase in SSIM was achieved while maintaining scan time.

Impact: The RRFCA utilising our novel calibration-free, GRAPPA-based, radial image reconstruction method provides a clinically relevant parallel imaging technique. In the future, our approach may incorporate compressed sensing to further reduce motion artifacts, particularly in applications like cardiac and dynamic MRI.

Keywords: Microstructure, Diffusion Tensor Imaging, oscillating gradient, diffusion time, spinal cord, multiple sclerosis

Motivation: Spinal cord MRI has both diagnostic and prognostic value for multiple sclerosis (MS) patients. Several quantitative MRI biomarkers show high sensitivity to characterize MS lesions but lack pathological specificity. Time-dependent DWI may reveal microstructural features and pathological variations in MS.

Goal(s): To explore diffusion time-dependence in the cervical spinal cord and its potential to quantify pathology of MS

Approach: Optimized oscillating gradient spin-echo (OGSE) DTI were performed for healthy volunteers (N=18) and MS patients (N=17).

Results: Diffusivities show time-dependence in the dorsal-columns and lateral-funiculis of healthy controls. The increase of RD in MS lesions is larger than healthy WM when diffusion time decreases.

Impact: The time-dependence of diffusivities in the cervical spinal cord of healthy volunteers and MS patients are observed using optimized OGSE DWI sequences on a clinical scanner. This may reveal further insight into the microstructural differences and pathological variations in MS.

Keywords: Bioeffects & Magnetic Fields, Multimodal, Spin-lock, Pulse sequence design, New Signal Preparation Schemes

Motivation: In-vivo use of Spin-lock (SL) rotary MR saturation contrast, despite encouraging phantom studies, raises questions about its sensitivity and practicality in neural magnetic field imaging.

Goal(s): Determine if SL contrast effectively maps human neuronal activation, evaluating its sensitivity and localization against MEG and 3T BOLD-fMRI.

Approach: Thirteen volunteers underwent SL-based scanning during visual stimulation, alongside BOLD and magnetoencephalography, with phantom experiments validating the paradigm and processing pipelines.

Results: Preliminary analysis revealed significant activation in the expected visual region for three subjects in SL contrast maps. Low detection was attributed to sensitivity limits estimated in the phantom, falling below MEG-estimated neural fields.

Impact: We assess Spin-lock 3T MR contrast for human neuronal activation mapping. Promising initial results highlight the need for refinement due to sensitivity limitations in neural field detection, supported by phantom MRI and MEG measures.

Keywords: Aging, Aging, Brain Age, Volumetry, White Matter Hyperintensity

Motivation: In an aging population, a subset of individuals at any age group present with low white matter hyperintensity (WMH) volume in the brain, while another subset has intermediate to high WMH load.

Goal(s): To establish a Brain Age Estimation model involving WMH lesion quantification as a clinical indicator of Brain Health.

Approach:  We have investigated the ‘Brain Health’ in terms of Brain Age using neuroanatomic volume, thickness together with WMH load across cognitively normal, impaired and Alzheimer’s Disease subjects.
 

Results: An increased Brain Age gap is observed for the subjects with elevated WMH load compared to the brains with low WMH.

Impact: Brain health is a composite representation of structural, fiber and vascular health. For the first time, a MR based quantitative platform with WMH load and comprehensive neuroanatomic volumetry is established, which estimates ‘Brain Age’ as an indicator of Brain Health.

Keywords: Fat & Fat/Water Separation, Fat

Motivation: Echo Planar Time-resolved Imaging (EPTI) can produce distortion- and blurring-free multi-echo images with high efficiency. For its broader application such as in body imaging, the challenge of fat suppression/separation needs to be addressed.

Goal(s): Achieving efficient water/fat separation using EPTI for high-quality fast multi-contrast/quantitative imaging in the presence of fat tissues.

Approach: In this study, water/fat separated EPTI (WFS-EPTI) was proposed to achieve this by: (1) designing a novel in-phase and out-of-phase EPTI acquisition and encoding scheme; and (2) adopting a k-space-based water/fat separation method.

Results: Experimental results demonstrated the efficacy of WFS-EPTI for water/fat separation and fat-robust distortion-free multi-contrast/quantitative imaging.

Impact: The proposed WFS-EPTI effectively separates water and fat signals, while providing efficient acquisition of high-resolution, distortion-free multi-contrast images and quantitative maps. It can extend EPTI to a broader range of applications.

Keywords: Low-Field MRI, Brain Connectivity, Connectomics

Motivation: Neuroscience MRI research, including assessment of structural connectomics, has been largely limited to high-resource settings.

Goal(s): To democratise assessment of brain connectivity by demonstrating the first ever diffusion-weighted imaging (DWI)-based connectomics at 64 mT.

Approach: 15-direction DWI data were acquired at 64 mT. Whole-brain tractograms were recovered after deep learning based denoising and constrained spherical deconvolution.  Whole-brain adjacency matrices and graph-theory parameters were extracted, and their test-retest agreement and variability assessed. For one subject, results were compared to high-field MRI.

Results: Global graph-theory parameters (e.g., small-worldness) showed high test-retest agreement. However, inter-hemispheric connectivity was overestimated at 64 mT compared to high-field results.

Impact: Our unique combinations of low-field (64 mT) diffusion-weighted imaging, denoising, spherical deconvolution and connectomics opens up new research opportunities, allowing the assessment of structural connectivity and network neuroscience studies of under-served populations where this has never previously been possible.

Keywords: Alzheimer's Disease, Alzheimer's Disease, Intermittent theta-burst stimulation (iTBS)

Motivation: Addressing the global AD crisis by investigating CT-iTBS as a non-pharmacological treatment to enhance memory and cognition.

Goal(s): To explore the therapeutic efficacy and determine the optimal treatment protocol of CT-iTBS in AD while unveiling its potential underlying mechanism for enhancing memory and cognitive functions.

Approach: Utilized brain magnetic resonance imaging analysis, behavioral tests, and immunofluorescence staining for assessing the therapeutic effect of different durations of CT-iTBS treatment.

Results: Prolonged CT-iTBS significantly enhanced cognitive and memory behaviors, altered brain functional connectivity, promoted a neuroprotective effect, and reduced amyloid accumulation in AD mouse model. These findings present a promising therapeutic avenue for AD patients.

Impact: Our findings revealed a highly promising avenue for enhancing the quality of life for individuals with AD and provided insights into the potential underlying neuroprotective mechanisms of CT-iTBS in alleviating memory deficits.

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, Conductivity

Motivation: To benchmark MR-Electrical Properties Tomography (MR-EPT) reconstruction methods.

Goal(s): To present an overview of the first MR-EPT reconstruction challenge participation and the results of its phase 1.

Approach: The challenge consisted of 3 phases:1) reconstructions from a simulated (blind) dataset (ground-truth EPs not provided);2) reconstructions from several simulated dataset (ground-truth EPs provided for few training dataset for tuning algorithm parameters);3) EPs reconstructions from measured data.

Results: 52 participants registered to the challenge; 39 submitted their results. For phase 1, all participants submitted a reconstructed conductivity map; 12 submitted a reconstructed permittivity map. The results show large variability in reconstruction accuracy and precision.

Impact: The results of phase 1 of the first MR-EPT reconstruction challenge show large variations in the estimated conductivity and permittivity maps demonstrating the need of benchmarking reconstruction methods on common datasets.

Keywords: Deuterium, Deuterium, Glucose Metabolic Rates

Motivation: Cerebral glucose metabolism via non-oxidative and oxidative pathways is critical for brain function, however, methods capable of quantitatively imaging metabolic rates are lacking. 

Goal(s): To develop a quantitative dynamic deuterium (²H) MRSI (DMRSI) method capable of mapping human brain glucose metabolic rates. 

Approach: Combining novel hardware and advanced post-processing method with kinetic models, we established a high-resolution, high-quality dynamic DMRSI capable of quantifying and imaging three metabolic rates of glucose consumption (CMR_Glc), lactate generation (CMR_Lac) and TCA cycle (V_TCA) in human brain at 7T.

Results: We demonstrate consistent whole-brain maps of CMR_Glc, CMR_Lac, V_TCA in health subjects.

Impact: We developed a novel DMRSI platform on an FDA-approved clinical 7T scanner that enables simultaneous high-resolution imaging of CMR_Glc, CMR_Lac and V_TCA  of entire human brain for the first time. This novel technology has potential for brain research and translation.

Keywords: Software Tools, Software Tools

Motivation: The ISMRM community is swiftly adopting data sharing and code review. While the advantages are clear, challenges persist in ensuring the quality and functionality of these shared resources.

Goal(s): To establish a platform for simplifying technical (or code) reviews and generating open-source living preprints with interactive data apps (e.g., dashboards).

Approach: We created NeuroLibre.org, offering dedicated cloud resources for hosting living preprints that combine narrative and executable content.

Results: NeuroLibre has published 8 living preprints, covering a variety of MRI applications. Each preprint is registered as citable and online-executable content with DOI links to archived reproducibility objects (code, runtime, data).

Impact: Our living preprints showcase how NeuroLibre helps reviewers interactively assess the quality and functionality of reproducibility objects effortlessly, bolstering the reproducibility of MRI publications. The ISMRM 2020 reproducibility challenge is our flagship example: https://doi.org/10.55458/neurolibre.00014

Keywords: Prostate, Prostate

Motivation: Pathogenic DDR gene alterations are associated with aggressive disease and poor outcomes among prostate cancer (PCa) patients.

Goal(s): To develop a radiomics-based pre-testing model for identifying DDR mutation carriers among PCa patients.

Approach: A total of 225 patients from three centers with both multiparameter MRI and genetic DDR mutations testing were included. Radiomic models were established based on T2WI and ADC sequences of MRI images. The predictive values were validated in both internal and external validation cohorts.

Results: The radiomics-based model exhibited an AUC of 0.835 in the training dataset, 0.824 in the internal validation dataset, and 0.836 in the external validation dataset.

Impact: In the current study, we introduced a noninvasive radiomics feature-based tool designed to predict pDDRg mutations in prostate cancer patients. External validation of the novel tool by datasets from other medical centers revealed a high predictive accuracy for pDDRg mutations.

Keywords: Valves, Valves, SPEEDI, sub-millisecond, high temporal resolution

Motivation: get-SPEEDI, a recently published pulse sequence, offers a promising MRI technique for imaging the rapid dynamics of the aortic valve (AV) opening and closing with sub-millisecond temporal resolution.

Goal(s): This study aims to assess the accuracy of measuring the AV dynamics with get-SPEEDI by comparing its performance with transthoracic ultrasound, which is the gold standard clinically.

Approach: Ultrasound echocardiograms and get-SPEEDI MR images of AV were acquired in healthy human subjects with 0.6- and 0.8-ms temporal resolution, respectively.

Results: There were no statistically significant differences between get-SPEEDI and ultrasound in the measurements of AV opening and closing dynamics and the maximum AV area.

Impact: The dynamic characteristics of the aortic valve measured with get-SPEEDI MRI agree well with the ultrasound measurements. get-SPEEDI MRI provides a new imaging tool for diagnosis of aortic valve diseases.

Keywords: Parkinson's Disease, Parkinson's Disease, susceptibility-weighted imaging; kinetics; iron accumulation; relaxometry covariance network; substantia nigra

Motivation: Iron deposition is implicated in the pathogenesis of Parkinson's disease (PD). However, most of the previous studies failed to report progressive iron accumulation with disease progression.

Goal(s): This study aimed to explore the kinetics of iron accumulation in the PD brain using a novel relaxometry covariance network (RCN) approach.

Approach: The RCN approach consisted of three steps, the identification of brain regions as propagators of iron, construction of causal RCN and individual differential RCN.

Results: The left substantia nigra pars reticulata, left substantia nigra pars compacta, and lobule VII of cerebellum vermis were identified as propagators of iron.

Impact: The application of our novel relaxometry covariance network on susceptibility-weighted imaging revealed iron accumulation kinetics in Parkinson's disease, which were closely related to the pathophysiological aspects of the disease. The current findings deserved further exploration to elucidate the underlying mechanisms.

Keywords: Alzheimer's Disease, MR Value

Motivation: Rising medical imaging utilization and increasing use of automated support systems demand high-quality, fast, and reproducible/robust MRI techniques. Despite rapid scanning afforded by deep learning, motion remains a common source of artifacts.

Goal(s): Integrate retrospective motion correction into a deep learning reconstruction to facilitate high-quality, fast, and motion-robust brain imaging.

Approach: Scout and guidance line-based motion correction was implemented into MPRAGE, SPACE and SWI to enable rapid motion trajectory estimation. A data-consistency driven neural network reconstruction was adapted to perform network regularized motion correction.

Results: Improved SNR and reduced motion artifacts are demonstrated in vivo using 4-6-fold accelerated scans with instructed subject motion.

Impact: Retrospective motion correction was integrated into a deep learning reconstruction to facilitate fast and motion-robust 3D brain imaging across T1, T2, T2 FLAIR and T2*/SWI. This should add clinical value to routine brain exams and emerging neuro-degenerative screening protocols (ARIA).

Keywords: Breast, Spectroscopy, lipids, cholesterol

Motivation: Adenoma-carcinoma cell models examined using 2D COSY recorded altered triglyceride, cholesterol and metabolites prior to malignant transformation.

Goal(s): Investigate if such chemical profiles are recorded in vivo in MRI apparently normal tissue in women with invasive cancer.

Approach: Nineteen women with invasive breast cancer were compared with healthy low risk controls. 

Results: Compared to controls, MRI normal tissue in cancer patients with low-density breasts recorded increases in cross peak F (68%), cholesterol (127%), and tumor promotor UDP-GlcNAc (81%). For dense breasts, increases recorded in cross peak F (47%), decreases in cholesterol (12%), triglyceride (56%) and double bonds (40%) in "switched-on" tissue.

Impact: Altered chemistry states, consistent with mechanisms leading to development of invasive carcinoma, are recorded in vivo from breast tissue distant to the cancer  compared to controls. These states are referred to as "switched-on" tissue and differ according to breast density. 

Keywords: Low-Field MRI, Low-Field MRI, Networking, Innovation

Motivation: Due to rising non-communicable diseases, limited MRI accessibility, and Africa's underrepresentation in ISMRM, the African Chapter (AC) was founded in 2023. An inaugural conference in Ghana focused on emerging MRI technology  for improved accessibility.

Goal(s): To provide the inaugural conference report of  AC-ISMRM, with the  identification of  challenges and barriers to MRI access and propose  solutions toward democratization of MRI across Africa.

Approach: A white paper approach was adopted

Results: Over 100  scientists from 12 African  countries  met to identify challenges and propose solutions for advancing MRI access and value in Africa. Low-field MRI was identified as a breakthrough innovation toward this goal.

Impact: The AC-ISMRM conference marks a pioneer event, convening African scientists and clinicians, aimed at establishing a network dedicated to rectify Africa's underrepresentation in MRI research, seeking solutions to challenges on the continent  and  promoting collaboration and MRI advancements 

Keywords: Liver, Low-Field MRI, Spiral, Dixon, abdomen

Motivation: Breath-held abdominal fat-suppressed imaging is challenging at mid- and low-field strengths (<1.5T). Fat saturation often fails due to the short T1 of lipid; and Cartesian Dixon imaging provides poor spatial resolution due to the need for long ∆TE, due to the smaller ∆f between water and lipid.

Goal(s): Breath-held fat-suppressed high-resolution volumetric abdominal imaging with T1 contrast.

Approach: Stack-of-Spirals Dixon imaging, with estimation and compensation for phase due to concomitant fields

Results: We demonstrate that spiral Dixon imaging at 0.55T makes excellent use of the required ∆TE, improving SNR efficiency and spatial resolution (1.7x1.7x5.0mm³) compared Cartesian Dixon (3.5x3.5x5.0mm³), within a 17-second breath-hold.

Impact: We demonstrate that spiral Dixon single breath-hold volumetric imaging is an attractive alternative to existing Cartesian-based methods for volumetric single breath-hold fat-suppressed imaging at 0.55T, as it simultaneously provides high-resolution and excellent fat-suppression.

Keywords: MR-Guided Radiotherapy, Radiotherapy, MRI-guided radiotherapy;

Motivation: Can we provide superior liver tumor visualization for online adaptive planning? MRI enables direct visualization of tumor and organs-at-risk (OAR). However, MRI contrast agents are often required to differentiate primary and metastatic liver malignant lesions from functional hepatic parenchyma. 

Goal(s): We employed super-paramagnetic iron oxide nanoparticles (SPION) as an MRI contrast agent.

Approach: SPION enhanced the liver-to-tumor contrast ration for rapid and accurate delineation of tumors and functional hepatic parenchyma throughout the entire treatment course. 

Results: This study is the first to report the efficiency of a single SPION injection for multi-fractionated MRI-guided liver stereotactic body radiotherapy on a 1.5T Elekta MR-Linac.

Impact: A single SPION injection significantly improved the tumor-to-liver contrast, and it was maintained throughout multi-fraction MRI-guided liver SBRT to provide rapid and accurate contouring tumor lesions from functional liver parenchyma for online adaptive planning.

Keywords: Tumors (Post-Treatment), Non-Proton, Sodium

Motivation: Sodium MRI is a promising technique for understanding the brain tumor microenvironment. However, sodium MRI at 3T suffers from extremely low SNR, resulting in compromised resolution and long acquisition times.

Goal(s): Our goal is to create a high-resolution sodium MRI at 3T using generative AI to improve biological characterization, treatment monitoring, and surgical planning for brain tumor patients.

Approach: We developed a physics-informed synthetic dataset to train an anatomically-constrained GAN for high-resolution neuroimaging of brain tumors.

Results: When applied to brain tumor patients' images, the synthetic-sodium MRI improved resolution, SNR, and correlated with expression of sodium-proton exchanger (NHE1) on image-guided biopsy.

Impact: High-resolution sodium neuroimaging at 3T using physics-informed anatomically-constrained GAN has the potential to make multinuclear MRI feasible in the clinical environment, leading to conceivable improvements in diagnosis, monitoring, treatment, and our understanding of the biology of brain tumors.

Keywords: Cartilage, MR Fingerprinting, Knee cartilage, Deep learning

Motivation: Estimating MRF quantitative parameters with neural networks (NNs) is faster than  dictionary-matching methods (DMs), and it has the advantage of providing continuously distributed parameters.

Goal(s): We investigate different aspects of NN training and evaluate its quantitative MRF performance and compare them with DMs.

Approach: We exploit how training data sizes, noise levels, and SVD compression sizes affect the MRF performance of the NNs and compare them with DMs.

Results: The NN provides a faster way of multi-parametric mapping from NIST/ISMRM phantom and knee joint MRF data sets with comparable performance to DMs.

Impact: Well-tuned NN is much more efficient for quantitative MRF, particularly for the knee joint. Besides computational speed, fine-tuning can also increase the performance and robustness to noise.

Keywords: Functional Connectivity, fMRI (resting state), functional connectivity, neuroscience, brain connectivity

Motivation: A current overarching challenge in neuroscience is to establish an integrated understanding of brain circuits and networks, particularly the interactions of neural populations across various spatiotemporal scales that give rise to functions and behavior. 

Goal(s): We posit that dissecting rsfMRI dynamics under direct single-pulse optogenetic modulation of thalamo-cortical networks will reveal critical insights into the functional architecture of rsfMRI networks. 

Approach: We deployed a computational approach (i.e., Gaussian PCA-HMM) to examine the organization of rsfMRI networks before and upon single-pulse stimulation of  thalamus. 

Results: We demonstrated a significant role of the basal forebrain and hypothalamus in regulating the transient dynamics of rsfMRI networks. 

Impact: The ability to directly perturb and model dynamics of rsfMRI networks present an unprecedented opportunity to understand brain-wide and higher-order circuits/networks, and their functions, which are difficult to probe using traditional behavioral and/or cognitive tasks and other neuroimaging approaches.

Keywords: Software Tools, Software Tools, Standardisation, Quality Control

Motivation: It is critical that MRI data acquired in multi-site, multi-vendor studies conforms to a standardised acquisition protocol.

Goal(s): To develop XNAT tools to highlight scans that do not conform to a specified protocol or are of insufficient quality, enabling rapid correction of errors before future scans.

Approach: Multi-site DICOM data is uploaded to XNAT after acquisition, by integrating software tools with this database, investigators are informed if data does not conform.

Results: DICOM-QC, a tool to automatically compare DICOM metadata to predefined values, and ImageSNR-QC to calculate image SNR, applied here to a multi-site kidney study.

Impact: This work outlines two tools that integrate with XNAT, DICOM-QC and ImageSNR-QC, which can be used by any investigators running large studies to ensure uploaded data conforms to the study protocol, ensuring consistency over sites, vendors, and repeated longitudinal scans.

Keywords: MR Fingerprinting, Quantitative Imaging, MR Fingerprinting, Compressed sensing, Image reconstruction, AI/ML Image Reconstruction

Motivation: Deep learning excels at compressed-sensing image reconstruction given large training datasets. Applying this paradigm to accelerated quantitative MRI, including magnetic resonance fingerprinting (MRF), is challenging because quantitative imaging datasets for training are scarce.

Goal(s): Can we overcome this limitation using new sources of training data from routine, largely available weighted-MRI images?

Approach: We introduce MRI2Qmap, a plug-and-play quantitative image reconstruction algorithm based on deep image denoising models pretrained on large multimodal weighted-MRI datasets.

Results: We showed, for the first time, that spatial/structural priors learned from independently-acquired datasets of routine weighted-MRI images can be effectively used for quantitative MRI image reconstruction.

Impact: Thanks to the widespread use of MRIs, our approach could enable much larger datasets to be used for training potentially enhanced AI models for fast quantitative MRI/MRF image reconstruction.

Keywords: Microstructure, Modelling, Immunotherapy, Liver, Tumours, Histology

Motivation: Multi-compartment liver diffusion MRI (dMRI) provides innovative markers of intra-cellular fraction (F) and cell size (CS). However, practical implementations for histologically-meaningful F and CS computation in the clinic are still sought.

Goal(s): To deliver a compact approach for F and CS estimation, informing model design with histology.

Approach: We compared 5 implementations of a standard two-compartment model for their ability to provide F and CS estimates that agree with reference biopsies in liver tumours.

Results: The best approach consisted of fitting a single-compartment model of intra-cellular diffusion to high b-value images. This provides promising metrics that stratify the risk of progression in immunotherapy.

Impact: We deliver a clinically-feasible liver diffusion MRI approach for intra-cellular fraction, cell size and density estimation. It consists of fitting a single-compartment model of restricted diffusion to high b-value images, and provides metrics that may inform on cancer immunotherapy response.

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Federated learning, multi-institutional, collaborative learning, image reconstruction

Motivation: Federated learning (FL) enables privacy-preserving training of deep reconstruction models across multiple sites to improve generalization at the expense of lower within-site performance. Yet, existing methods require a common model architecture across sites, limiting flexibility.

Goal(s): Our goal was to devise an architecture-agnostic method for collaborative training of heterogeneous models across sites.

Approach: We introduced a novel peer-to-peer generative learning method (PGL-FedMR), where individual sites share a generative prior for their MRI data with remaining sites, and prior-driven synthetic data are used to train reconstruction models at each site.

Results: PGL-FedMR improves across-site generalization over local models, and within-site performance over conventional FL.

Impact: Improvements in within-site and across-site performance for MRI reconstruction through PGL-FedMR, coupled with the ability to handle heterogeneous architectures, may facilitate privacy-preserving multi-institutional collaborations to build reliable reconstruction models for many applications where data are scarce including rare diseases.

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence

Motivation: DT-CMR can revolutionise diagnosis and treatment of heart conditions by non-invasively imaging cardiomyocyte microstructure, but currently long acquisition times prevent clinical use.

Goal(s): Reduce the number of breath-holds required for in-vivo DT-CMR acquisitions, resulting in significantly reduced scan times with minimal image quality loss.

Approach: We developed a deep learning model based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning to de-noise diffusion tensors computed from reduced-repetition DT-CMR data. We compared model performance to conventional linear fitting methods and a baseline deep learning approach.

Results: Our model reduced noise over 20% compared to previous state-of-the-art approaches while retaining known clinically-relevant myocardial properties.

Impact: This breakthrough in DT-CMR acquisition efficiency could enable rapid microstructural phenotyping of the myocardium in the clinic for the first time, revolutionising personalised diagnosis and treatment by unlocking DT-CMR’s ability to non-invasively characterise heart muscle organisation at the cellular level.

Keywords: Hybrid & Novel Systems Technology, Hybrid & Novel Systems Technology, New Devices, Gradients, Magnets, Head-Only

Motivation: MRI has evolved into an indispensable tool, but remains inaccessible to much of the world’s population.

Goal(s): To build a compact, low-cost, mid- to high-field MRI system capable of producing diagnostic-quality images.

Approach: A complete redesign of MR scanner architecture and key technologies; including a compact high temperature superconducting magnet, multi-coil gradient array, and digital spectrometer. The system required extensive testing prior to integration and initial imaging.

Results: Initial experiments produced high-resolution images despite using an extremely inhomogeneous magnetic field from the compact 0.7 tesla magnet.

Impact: This work represents a significant milestone within the MRI community to address the problems in accessibility and under-utilization facing MRI today. By focusing on ways to develop portable, low-cost systems, the accessibility of this imaging modality can increase substantially.

Keywords: Biology, Models, Methods, Diffusion/other diffusion imaging techniques, inflammation, brain, degeneration, demyelination

Motivation: Validation of MRI-extracted biomarkers is seldom performed, and when available,
Is normally underpowered and based on correlation.

Goal(s): Here we present an innovative framework for validating microstructural MRI biomarkers by eliciting cell-specific responses.

Approach: The framework is based on injection of neurotoxins in rats, followed by MRI exploration and histology.

Results: We successfully isolated conditions associated to neurodegenerative, demyelinating and inflammatory pathologies and demonstrated sensitivity and specificity of MRI- derived biomarkers.

Impact: This framework impulse a much-needed change in paradigm for MRI validation by challenging the biological content of MRI derived biomarkers, refine and test new models for microstructural imaging and bridge the gap between advances in MRI physics and clinical applications.

Keywords: Motion Correction, Fetus, retrospective-gating

Motivation: Cardiac synchronization in adult and fetal imaging requires external devices (electrocardiogram, Doppler-ultrasound), which may compromise image quality and increase scan time. Self-gating with real-time imaging can mitigate this but may be less reliable for irregular motions and limited in fetal applications.

Goal(s): To develop a fast image-based cardiac phase estimation method with no assumption on the heart rate and minimal user input.

Approach: Dynamic Mode Decomposition is used to estimate cardiac motion signal for retrospective-gating.

Results: DMD cardiac phase estimation captures cardiac motion despite the irregularities and other bulk motions, as demonstrated in real-time adult and fetal cardiac imaging, including a twin gestation.

Impact: The proposed technique, Dynamic Mode Decomposition cardiac phase estimation, constructs cardiac signal with no assumption on periodicity, no iterations, and only minimal user input. This may be valuable in fetal cardiac imaging, where the cardiac signal is not readily available.

Keywords: Whole Joint, Molecular Imaging, Knee, Pain

Motivation: Diagnosis of chronic knee pain remains a challenge with conventional diagnostic methods leading to unsatisfactory treatment in a large group of patients.

Goal(s): To investigate the use of sigma-1 receptor (S1R) radioligand, [¹⁸F] FTC-146 in conjunction with positron emission tomography/magnetic resonance imaging (PET/MRI) for identifying the pain generator in chronic knee pain.

Approach: Comparison of [¹⁸F] FTC-146 PET-MRI imaging findings in patients with unresolved chronic knee pain to healthy volunteers.

Results: All 15 patients showed statistically significant increased uptake of S1R compared to healthy control subjects in a variety of locations. At sites of abnormal PET uptake, MRI often did not demonstrate abnormalities. 

Impact: Future clinical implementation of S1R-PET/MR can potentially help reveal previously unidentified pain generator in patients with chronic knee pain that have exhausted standard clinical care leading to better-targeted treatment. 

Keywords: Safety, Safety, Deep brain stimulation, Active implantable medical devices, RF safety

Motivation: MRI of neurostimulators is severely constrained due to RF safety concerns.

Goal(s): Demonstrate that built-in sensors in commercial devices, such as a deep brain stimulator, can provide all necessary information to detect and improve RF safety. 

Approach: We investigated and utilized built-in impedance measurements of two commercial DBS systems for the detection and mitigation of RF-induced currents on the electrodes of a DBS lead. 

Results: Impedance measurements were correlated at various RF power levels. Temperature rise at the tip of DBS electrodes could be reduced to 0.02 K from 17.14 K at the same total powers (16.85±0.45 W).

Impact: Our demonstration of mitigation of RF-induced heating in active implants through built-in sensor measurements from a commercial DBS system indicated up to ~850× improvement in temperature rise proving the unmet value of sensors for MR imaging patients with active implants.

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Image reconstruction, diffusion models, deep learning

Motivation: Diffusion models can reconstruct high-quality MR images, but their training neglects physical constraints and requires supervision via ground-truth images derived from fully-sampled acquisitions.

Goal(s): Our goal was to devise a diffusion-based method that incorporates physical constraints and that can be trained using undersampled acquisitions.

Approach: We introduced a novel diffusion model (SSDiffRecon) based on a physics-driven unrolled transformer architecture; and self-supervised training was achieved by predicting held-out subsets of acquired k-space data from remaining subsets.

Results: SSDiffRecon achieved superior reconstructions to alternative self-supervised methods, and performed on par with a supervised benchmark trained on fully-sampled acquisitions.

Impact: The improvement in image quality and acquisition speed through SSDiffRecon, combined with the ability to train on undersampled acquisitions, may facilitate adoption of AI-based reconstruction for comprehensive MRI exams in many applications, particularly in pediatric and elderly populations.

Keywords: Fetal, Motion Correction

Motivation: Maternal breathing and fetal bulk motion frequently limit the utility of fetal 4D flow MRI. 

Goal(s): To demonstrate the effects of maternal respiratory and fetal bulk motion correction on 4D flow MRI 

Approach: Prospective undersampled fetal 4D flow data were acquired in two subjects, followed by compressed sensing reconstruction that included maternal respiratory gating and bulk motion correction. Standard SENSE-accelerated 4D flow acquisitions without motion correction (N=22) provided reference for the ability to quantify flow. 

Results: Comparisons of the motion corrected data to normative performance illustrate the technique’s potential for mitigating motion in fetal 4D flow, with equivalence to standard SENSE accelerated scans. 

Impact: The proposed sequence and flexible reconstruction workflow provide motion robustness for fetal 4D flow MRI. Further exploration of motion correction techniques has potential to enhance spatial and temporal resolution and to mitigate motion-related errors over extended scanning durations.

Keywords: Functional Connectivity, Brain Connectivity, BOLD, difusion fMRI, visual system, connective field model

Motivation: To disentangle feedback and feedforward signals in cortical circuits.     

Goal(s): To unravel the intricate neural connections within cortical layers.

Approach: We implemented a layer connective field (lCF) model and applied it to ultrafast RS data and RS dfMRI data.

Results: 1.Intracortical lCF shows two lCF size profiles: feedforward with inverse U shape with the larger lCF sizes at layer 5 and feedback with U shape and larger CF sizes at superficial and deeper layers. 2.In the absence of visual input the functional connectivity reflects visuotopic organization. 3.lCF estimates obtained from dfMRI(ADC) are more layer specific than the ones estimated from BOLD.

Impact: This study showcases the ability of high spatio-temporal resolution MRI techniques (ultrafast BOLD and dfMRI) when coupled with biologically grounded connectivity models (lCF) to unveil the intricacies of information directionality within topographically organized cortices.

Keywords: Functional/Dynamic, Muscle, Genetic Diseases

Motivation: Changes to muscle twitch dynamics are overlooked in trials assessing resistance exercise in primary mitochondrial myopathies (PMM).

Goal(s): Motor unit MRI to measure twitch dynamics in PMM participants before and after a 12-week exercise programme.

Approach: Voxel-wise measurements of rise time (T_rise), contraction time (T_contract) and half-relaxation time (T_half-relax) in the tibialis anterior in 10 controls and 9 PMM participants. PMM participants scanned twice, before and after a 12-week exercise programme.

Results: T_contract of the tibialis anterior was significantly longer in PMM participants post exercise; T_rise, T_half-relax demonstrated no change. In participants who had the highest adherence to exercise T_contract increased the most.

Impact: Motor unit MRI (MUMRI) detected slower muscle contraction times in primary mitochondrial myopathies post resistance exercise programme. This may evidence increased numbers of type-I fibres post-exercise. MUMRI could be used to measure changes in muscle twitch dynamics in neuromuscular diseases.

Keywords: Gradients, Gradients, Diffusion Acquisition, Neuro

Motivation: Current human MR scanners cannot resolve the full range of length scales needed to study the brain's microscopic and mesoscopic structure.

Goal(s): To construct and validate the next-generation human connectomics and microstructure MRI scanner known as Connectome 2.0.

Approach: The 3T Connectome 2.0 scanner incorporates a peripheral nerve stimulation-optimized asymmetric head gradient driven by dual gradient power amplifiers. Custom-built high-sensitivity 72-channel (in vivo imaging) and 64-channel (ex vivo imaging) receive coils were integrated.

Results: The Connectome 2.0 scanner achieves G_max=500 mT/m and SR_max=600 T/m/s, demonstrates 2x improved SNR for diffusion MRI over Connectome 1.0, and enables high-resolution tractography.

Impact: The Connectome 2.0 scanner will allow the exploration of new microstructure properties and connectional anatomy in the living human brain with unprecedented spatial and diffusion resolution.

Keywords: Liver, fMRI, Hepatocellular Carcinoma； Immunotherapy； Nanocarrier; IVIM-MRI; Tumor Microenvironment

Motivation: To enhance the efficacy of hepatocellular carcinoma immunotherapy using a nanocarrier and to explore IVIM-MRI for monitoring the tumor immune microenvironment.

Goal(s): To synthesize iRGD-targeted liposomes to enhance the treatment efficacy of hepatocellular carcinoma and to develop effective biomarkers for the tumor microenvironment.

Approach: We synthesized iRGD-modified liposomal co-encapsulating Lenvatinib and BMS-202. IVIM-MRI was performed before and at 6 and 12 days after treatments, followed by pathological examination after the final scan.

Results:  iRGD-lip@Len/BMS-202 promotes tumor vascular normalization and effectively activates an anti-tumor immune response. Importantly, the derived parameters D* and f are significantly correlated with tumor vascular normalization and immune activation.

Impact: The iRGD-targeted dual-drug liposomal nanoparticles exhibited potent synergistic anti-tumor effects. Additionally, IVIM-MRI facilitated the monitoring of changes in the tumor microenvironment, with the D* and f parameters serving as valuable indicators for evaluating tumor vascular network and immune microenvironment modulation.

Keywords: MR Fingerprinting, MR Fingerprinting, 31P, MRF, creatine kinase rate, kCK, MRSI, 7T

Motivation: Using ³¹P MRS combined with magnetization transfer (MT) experiments including saturation transfer or inversion transfer to assess chemical exchange rate of creatine kinase (k_CK) in the human brain are time-consuming and limited to 1D-acquisitions.

Goal(s): Acquiring a 3D whole brain k_CK map.

Approach: In this abstract, we introduce an advanced, fast 3D-³¹P-MRF sequence for the human brain at 7T.

Results: The novel 3D-³¹P-MRF approach is feasible for whole brain mapping of k_CK, enabling the investigation of region-specific energy metabolism under various pathological conditions.

Impact: Using the novel 3D-³¹P-MR Fingerprinting approach for whole brain mapping of k_CK enables us to investigate region-specific energy metabolism under various pathological conditions and may enhance our understanding of the underlying molecular and metabolic processes.

Keywords: Simulation/Validation, Microstructure, simulations, validation

Motivation: The non-monotonic dependence of the diffusion kurtosis on diffusion time has been observed in tissue, yet its relation to membrane integrity and tissue geometry remains unknown.

Goal(s): We investigate the relation between the characteristic time  t_peak and the tissue parameters, such as cell size, volume fraction and permeability.

Approach: We perform Monte Carlo simulations of diffusion and exchange in randomly, densely packed spheres with varying permeability, cell fractions and sizes, and identify the value of t_peak.

Results: We obtain an empirical, albeit highly accurate relation of t_peak to tissue parameters in a broad parameter range.

Impact: Diffusion-kurtosis time-dependence is sensitive to pathological changes in membrane integrity and cellular structure in diseases, such as ischemic stroke and tumors. Numerical simulations suggest an empirical interpretation of kurtosis time-dependence, offering a novel biomarker for in vivo evaluation of pathology.

Keywords: Diagnosis/Prediction, Data Analysis, Deep Learning

Motivation: Current deep learning methods for assessing knee osteoarthritis have limitations in learning long-range spatial information from magnetic resonance imaging (MRI).

Goal(s): This study aims to develop a new deep learning model for total knee replacement (TKR) prediction using MRI.

Approach: We proposed a novel transformer-based model, MR-Transformer, adapted from the ImageNet pre-trained vision transformer DeiT-Ti. The model can capture long-range spatial information from MR images with transformer architecture. We evaluated our model on TKR prediction using MR images with different tissue contrasts.

Results: The experimental results demonstrated an improved performance of MR-Transformer compared to conventional deep learning models.

Impact: Our proposed MR-Transformer enhances computer-aided diagnosis accuracy in total knee replacement prediction using MRI. It has the potential to provide rapid and quality diagnostic outcomes, assisting physicians in making timely and informed treatment decisions.

Keywords: Diffusion Acquisition, Diffusion/other diffusion imaging techniques

Motivation: Current distortion-free multishot diffusion MRI (dMRI) techniques rely on interim reconstructions to estimate a fieldmap, whose quality deteriorates at high accelerations, thus precluding high-resolution imaging.

Goal(s): To develop a distortion-free acquisition that reaches high accelerations with high fidelity.

Approach: We propose PRIME, which incorporates a second echo acquired at lower resolution and acceleration, but with matching echo spacing as the first echo. This yields high-fidelity fieldmaps to be used in 10-fold accelerated scans.

Results: PRIME enables high-quality distortion-free dMRI at R_inplane×SMS=5×2 and 1mm³ resolution without prolonging the scan thanks to utilizing the dead time in gSlider RF-encoded acquisitions.

Impact: We propose a distortion-free dMRI sequence, PRIME, that reaches R_inplane×SMS=5×2 at 1mm³ resolution with high fidelity owing to its ability to estimate a high-quality fieldmap from a second echo inserted without prolonging the TR in gSlider RF-encoded acquisitions.

Keywords: Segmentation, Segmentation

Motivation: Whole-brain MRI parcellation serves as a feature extraction technique, allowing for the condensation of over a million pixels of information into a few hundred neuroanatomically defined elements.

Goal(s): The multi-atlas label-fusion (MALF) method is known for accurate parcellation but typically necessitates several hours to process a single image. Our goal was to develop a faster parcellation tool with an accuracy comparable to that of MALF.

Approach: We introduce open-source multiple anatomical parcellation T1 (OpenMAP-T1), based on deep learning and multi-processing.

Results: The OpenMAP achieves an equivalent parcellation performance to MALF and is 40 times faster.

Impact: OpenMAP significantly accelerates processing speed, allowing for large-scale data analysis using volumetric information derived from detailed parcellation of the whole brain, including both gray and white matter regions.

Keywords: Dementia, Neuro, magnetic resonance spectroscopy, progressive supranuclear palsy, astrocyte reactivity

Motivation: Although astrocytic pathology is a pathological hallmark of progressive supranuclear palsy (PSP), the role of astrocytes in the pathophysiology of PSP is not fully understood.

Goal(s): This study aimed to evaluate astrocyte reactivity in vivo in patients with PSP.

Approach: Astrocyte reactivity was assessed by magnetic resonance spectroscopy and plasma biomarkers, which were verified via tau-PET and histopathological analysis.

Results: Our results suggest that, in the anterior cingulate cortex, astrocyte reactivity precedes pronounced tau deposition and neurodegenerative processes and modulates brain function in PSP. Elevated myo-inositol was associated with high lactate levels, suggesting a link between reactive astrocytes and brain energy metabolism changes.

Impact: This study assessed astrocyte reactivity in vivo using magnetic resonance spectroscopy and plasma biomarkers, providing insights into the involvement of astrocytes in the pathogenesis of progressive supranuclear palsy.

Keywords: Peripheral Nerves, Nerves, Pacinian corpuscles

Motivation: Exploit the high resolution provided by the 7T MRI technology to detect fine structures in the hand.

Goal(s): To create an atlas of hand structures, with a specific focus on nerves and Pacinian corpuscles. This atlas is intended to serve both diagnostic purposes and to support reconstructive surgical procedures.

Approach: An ethics committee was obtained to scan volunteers using 7T MRI. Post-processing was carried out to delineate the nerve fiber network and mechanoreceptors.

Results: We successfully reconstruct and describe the anatomy of all nerve fibers from the carpus to the digital nerve division, as well as the Pacinian corpuscles, for three healthy volunteers.

Impact: A visual interactive “Hand Nerves Atlas” matching morphology and fiber tracking of hand nerves on high-field will be delivered to the scientific community for fundamental research, to clinicians for microscopic surgery of nerves, and for educational purposes in medical schools.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: Longitudinal MRI scans performed on the same patient offer valuable temporal redundancy that can be exploited for image reconstruction. However, this wealth of information is usually ignored in current clinical practice, with data from different sessions typically reconstructed separately.

Goal(s): This study introduces a longitudinal dynamic MRI framework that leverages temporal correlations across multiple imaging sessions to improve image reconstruction.

Approach: Our reconstruction approach aims to reconstruct multi-session data jointly as a dynamic image series employing a combination of low-rank subspace and spatiotemporal constraints. 

Results: The initial results demonstrate that joint longitudinal reconstruction outperforms standard separate reconstructions, which may allow for additional acceleration. 

Impact: By exploiting image correlations across multiple sessions, our longitudinal dynamic MRI framework can improve image reconstruction and enable higher acceleration compared to standard separate reconstruction. 

Keywords: CEST / APT / NOE, CEST & MT, B1, B0, WASABI, data processing, UHF, 7T, tools

Motivation: WASABI provides high fidelity B₀ and B₁ maps necessary for CEST correction yet suffers from prolonged post-processing incompatible with clinical use. 

Goal(s): Our goal was to design an optimisation-free method to expedite map estimations.

Approach: A direct relationship was derived between B₀ and B₁ and information in WASABI Z-spectra.

Results: The proposed approach accelerated post-processing by a factor of 80, with improved estimation in brain regions that are noisy and/or have unpredictable initial magnetisation.

Impact: Improvement in speed and accuracy provided by RADISH has the ability to make WASABI, and quantitative CEST at ultra-high-field in general, more reliable and clinically feasible.

Keywords: Small Animals, Vessels, Glymphatic

Motivation: The perivascular space (PVS) plays a crucial role in facilitating the clearance of waste products and the exchange of cerebrospinal fluid and interstitial fluid in the central nervous system.

Goal(s): However, the limited depth penetration of current imaging methods impedes the study of glymphatic dynamics in deep brain regions.

Approach: In this study, we introduced an ultra-high-resolution dynamic contrast-enhanced MRI mapping approach based on single-vessel multi-gradient-echo methods.

Results: This technique allowed the differentiation of penetrating arterioles and venules from adjacent parenchymal tissue voxels and enabled the detection of Gd-enhanced signals coupled to PVS of penetrating arterioles in the deep cortex and hippocampus.

Impact: The study revealed significant PVS-specific Gd signal enhancements, shedding light on glymphatic function in deep brain regions. These findings advance our understanding of brain-wide glymphatic dynamics and impaired waste clearance, warranting further exploration of their clinical relevance and therapeutic applications.

Keywords: Tractography, Tractography & Fibre Modelling, Multimodal, Microscopy, structural connectivity, diffusion, machine learning

Motivation: Joint modelling of diffusion MRI and microscopy can leverage their complementary strengths to improve the estimation of fibre orientations. Ideally, these benefits would extend beyond the few datasets where dMRI and microscopy are acquired in the same brain to improve orientation estimates in in-vivo data.

Goal(s): To translate the unique properties of joint dMRI-microscopy data modelling to benefit in-vivo dMRI datasets.

Approach: We construct a domain adaptation adversarial network that can estimate microscopy-informed FODs from single-shell in-vivo dMRI.

Results: Tractography performed using network-derived FODs show improved tracking in grey matter, bottleneck regions, superficial white matter fibres, and long-range structural connectivity.

Impact: Our microscopy-informed neural network improves fibre orientation estimation from in-vivo single-shell dMRI datasets. We demonstrate improvements in fibre tracking that may enable more precise and detailed detection of connectivity, with a broad range of applications in basic and clinical neuroscience.

Keywords: New Trajectories & Spatial Encoding Methods, Data Acquisition, low-field MRI, RF encoding, New spatial encoding, Bloch Siegert shift, STAR

Motivation: Eliminating conventional gradients can help miniaturize and lower costs of MRI significantly. No method using RF-gradients has been able to achieve frequency encoding, the fastest encoding mechanism in MRI. 

Goal(s): Develop a Simultaneous-Transmit-and-Receive (STAR) system and perform RF Frequency encoding using the Bloch Siegert shift.

Approach: A novel injection transformer, 2MHz/47.5mT RF coil setup and pulse sequence was developed to enable STAR to prevent the RF encoding signal from overwhelming the receiver while frequency encoding MR signal without conventional gradients. 

Results: The novel STAR system achieved 99.75% cancellation of RF encoding signal, enabling the first-ever acquisition of frequency encoded MR images using RF-gradients. 

Impact: We have demonstrated, for the first time ever, frequency encoded MRI using RF field gradients in place of conventional B₀ gradients. This is a fundamental requirement to make RF encoded-MR imaging as fast as conventional gradient encoding. 

Keywords: Normal Development, Microstructure, Development, cortex, childhood, adolescence

Motivation: The adolescent brain has been well described using MRI, revealing ongoing cortical thinning and volume loss. But which underlying cellular properties drive these changes?

Goal(s): To model developmental patterns of soma and neurite architecture in the human cerebral cortex.

Approach: We quantified in vivo cortical neurite and soma microstructure in a sample of children and adolescents aged 8-18 years. We then analysed two human gene expression databases to determine cell-type specific profiles underlying these MR-based changes.

Results: Developmental increases in neurite density and reductions in soma radius suggest increasing cortical oligodendrocyte density, supporting the model of protracted intra-cortical myelination throughout the adolescent period.

Impact: Our novel study suggests that ongoing intracortical myelination underpins developmental patterns of cortical neurite and soma microstructure. Once thought to be driven by synaptic pruning, increasing cortical oligodendrocyte density may underlie previously reported patterns of cortical volume loss in adolescence.

Keywords: Adolescents, Adolescents, screen use, reading, brain volume, brain development

Motivation: The causal relationships between screen use and mental health were not clear.

Goal(s): We used genetic, imaging, and questionnaire data from ABCD study to investigate the causal relationships between screen use and mental health in early adolescents.

Approach: One-sample Mendelian randomization analysis.

Results: We found a direct causal relationship between screen use and behavior problems and an indirect effect between screen use and brain volume by the changes in reading habits. 

Impact: These findings provide new evidence for a causal influence of screen use and reading habits on brain development and highlight the importance of monitoring media use and related habits change in children.

Keywords: Cancer, Cancer

Motivation: Platinum resistance of high-grade serous ovarian carcinoma (HGSOC) is related to tumor heterogeneity. Multi-omics integration can complement tumor heterogeneity at multiple scales and  enhance the predictive power of single models.

Goal(s): We aimed to explore a range of diverse multi-omics models to predict platinum resistance of HGSOC.

Approach: Multi-omics models were  developed and validated using MRI-based habitat radiomics, pathomics based on haematoxylin and eosin (H&E)-stained whole slide images (WSIs), and clinical parameters.

Results: Among the array of single and composite models, the Clinic_Habitat model exhibited the most promising predictive performance, with the Clinic_Habitat_Pathology model ranking as the second-best performer.

Impact: This study carries the potential to equip clinicians with treatment strategies aimed at enhancing the efficacy of individualized therapy.

Keywords: Myocardium, Perfusion

Motivation: This research advances quantitative analysis of myocardial perfusion MRI, potentially enhancing the precision of coronary microvascular disease diagnosis.

Goal(s): To establish a novel spatiotemporal radiomics and fractal analysis approach, assessing myocardial perfusion patterns and complexity throughout all temporal frames of stress MRI.

Approach: Employing free-breathing stress myocardial perfusion MRI, we utilized a comprehensive pixel-by-pixel spatiotemporal feature extraction, alongside phase-specific analysis, to derive global and segmental perfusion insights.

Results: Preliminary results indicate our method's efficacy in motion correction and feature extraction, offering a new quantitative perspective on myocardial perfusion, potentially relevant for CMD assessment.

Impact: The study introduces a framework that quantitatively captures myocardial perfusion patterns, potentially paving the way for enhanced diagnostic methods in coronary microvascular disease and facilitating a more precise approach to patient assessment. 

Keywords: Heart Failure, Fat, T1 Mapping

Motivation: Proinflammatory epicardial adipose tissue (EAT) contributes to heart failure (HF). MRI fatty acid composition (FAC) and T₁ of EAT may distinguish proinflammatory vs. healthy EAT. Applying separate FAC and T₁ mapping sequences is time consuming, motivating the development of accelerated methods.

Goal(s): Our goal was to create an accelerated joint EAT FAC and T₁-mapping method (FACT) for use in mice at 9.4 T.

Approach: An inversion-recovery multi-echo sequence and model-based mapping method was developed with acceleration along orthogonal time dimensions. 

Results: Results demonstrate feasibility of the FACT method with approximately rate 12 acceleration. 

Impact: The FACT method efficiently and accurately determines both EAT fat composition and T₁ and could be used in-vivo to investigate mechanisms and efficacy of novel therapies targeting proinflammatory EAT in the context of metabolic heart disease.

Keywords: Analysis/Processing, Brain, automatic scan planning

Motivation: Manual scan planning in clinical MRI is inaccurate, inconsistent and time-consuming.

Goal(s): A deep learning-based end-to-end automated scan planning framework has been developed for MRI head scans.

Approach: We propose a two-stage end-to-end 3D cascaded convolutional network framework, called 3D CFP-UNet, which localizes the positions of five key anatomical landmarks and achieves a coarse-to-fine result. We also propose loss functions PRL and DRL with physical meaning in automatic scan planning. 

Results: Our approach yields satisfactory scan planning results on 229 test subjects, with PAE and PRE reaching 0.872mm and 0.10%, respectively.

Impact: MRI automated scan planning can help improve scan efficiency. Also, it improves scan consistency for follow-up comparisons.

Keywords: Psychiatric Disorders, Psychiatric Disorders, major depressive disorder, fMRI, functional gradient, subcortical structure, cognition

Motivation: The continuous spatial patterns of inter-region connectivity within the subcortical network still remain less well-understood in MDD.

Goal(s): Using functional gradient mapping, a novel approach to identify hierarchical organization of functional networks, we aim to evaluate multiscale subcortical gradients in MDD and their association with cognition.
 

Approach: Subcortical gradient alterations at the global-, system-, and subregion-levels and their relation to neuropsychological functioning were assessed in MDD patients relative to healthy controls.

Results: Principal gradient values were lower in thalamic and limbic systems but higher in basal ganglia in MDD. Interactions between thalamic and basal ganglia gradient alterations were implicated in MDD-related memory impairments.

Impact: Multiscale subcortical gradient alterations can enhance our understanding of MDD-related hierarchical disturbances in subcortical function and may provide useful clinical biomarkers for cognitive impairments in MDD.

Keywords: Psychiatric Disorders, Brain

Motivation: Suicide-related connectomic signatures in depression and underlying transcriptional patterns have been poorly understood, most previous findings were limited by small-sample and single-site design.

Goal(s): To identify robust brain structural network deficits associated with suicidal thoughts and behaviors (STB) in major depressive disorder (MDD) and to determine related transcriptional profiles.

Approach: Based on mutlicenter MRI data of over 700 individuals, group-level connectomic comparisons and connectome-transcriptome association were analyzed.

Results: Robust structural connectomic alterations associated with STB in MDD were distributed in the prefrontal, limbic and temporal areas. STB-related connectomic alterations were spatially correlated with genes enriched for cellular metabolism and synaptic signaling.

Impact: These findings reveal a robust pattern of brain structural deficits at network level and demonstrate its linkage to gene expression patterns, which provides novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of STB.

Keywords: Arterial Spin Labelling, Perfusion, Cerebral blood flow, arterial transit time, multi time point, arterial spin labeling

Motivation: Existing multi-timepoint arterial spin labeling (ASL) methods can only estimate cerebral blood flow (CBF) and arterial transit time (ATT) with a limited range of ATT (<2000ms).

Goal(s): Improve quantification of CBF and ATT for a wide range of ATT.

Approach: MULTIVERSE ASL applies combined fitting of multi-PLD pseudo-continuous (PC) ASL and multi-PLD velocity-selective (VS) ASL to measure CBF and ATT.

Results: With the same scan time, MULTIVERSE ASL improved the accuracy and precision and reduced uncertainty in CBF and ATT quantification across an extended range of ATT (500-4000ms).

Impact: This novel and straightforward approach improves perfusion measurement over the extended range of arterial transit time which was not possible with existing ASL methods. It highlights the clinical potential of ASL-based perfusion mapping in various altered physiological and pathological conditions.

Keywords: Thermometry/Thermotherapy, MR-Guided Interventions, Liver, Motion Correction, Radial MRI, Focused Ultrasound

Motivation: MRI thermometry faces challenges in moving tissues: intra- and inter-scan motion, limited spatio-temporal resolution, and constrained spatial coverage. These obstacles result in temperature mis-calculations, compromising treatment safety and efficacy.  

Goal(s): To develop an image-navigated 3D thermometry method to simultaneously track respiratory motion and temperature in moving tissue.

Approach: A stack-of-radial sequence was combined with compressed sensing reconstruction to obtain dynamic 3D images. An image-navigated multi-baseline proton resonance frequency shift (PRF) method was developed to generate motion-resolved temperature maps with tissue tracking.

Results: The proposed method achieved 24-30 slice coverage with a temporal resolution <1 second/volume and mean absolute error <2 degrees during motion.

Impact: The proposed method could improve the safety and efficacy of MRI-guided thermal therapies through reliable temperature monitoring in moving tissues. The capability to simultaneously track motion and temperature evolution enables feedback control, including focused ultrasound beam steering in moving organs.

Keywords: fMRI Acquisition, Data Acquisition, fMRI

Motivation: Achieving millisecond-scale temporal resolution MRI has the potential to provide exciting insights into fast functional/physiological processes of the brain. 

Goal(s): Develop a new acquisition method, EPTIMA, that can achieve millisecond-scale temporal resolution, while improving efficiency by acquiring a time-series trial of 2D-images in a single excitation for high robustness to physiological-noise/motion.

Approach: EPTIMA captures fast temporal dynamics occurring within the readout by measuring the rate at which the baseline signal evolution is changing, and employs spatiotemporal encodings to acquire a complete time-series trial in a single-excitation.

Results: EPTIMA can image rapid electric current changes in a phantom and resolve stable phase/magnitude changes in-vivo.

Impact: A new acquisition, EPTIMA, was developed to achieve millisecond-scale temporal resolution and to image ultra-fast dynamic processes of human brain. It improves efficiency by acquiring a time-series trial of 2D-images in a single excitation with high robustness to motion/physiological noises.

Keywords: Multimodal, High-Field MRI, EEG, fMRI, EEG-fMRI, 7T, laminar

Motivation: The combination of BOLD-fMRI at 7T with EEG could bring novel insights to neuroscience. However, the combination has remained challenging due to accentuated artifacts and RF-coil constraints.

Goal(s): To implement a first-of-its-kind 7T EEG-fMRI framework combining key developments from recent studies, and assess its safety, data quality and functional sensitivity in humans.

Approach: Extensive tests in phantom and humans(N=8) including field mapping, structural MRI and fMRI (1.6 and 0.8mm-resolution) acquired with+without EEG. Comparisons of data quality and functional sensitivity.

Results: The framework proved safe and feasible with fMRI down to sub-mm resolution, with moderate quality losses and potentially negligible impact on functional sensitivity.

Impact: This study characterizes the feasibility of 7T-EEG-fMRI with high sensitivity and acceleration capabilities, which could bring valuable insights to research in e.g. laminar functional connectivity, or localization of epileptogenic sources and their propagation pathways, for clinical diagnostic and pre-surgical planning.

Keywords: Quantitative Imaging, Quantitative Imaging, phase processing, background removal, deep learning, image decomposition, phase unwrapping

Motivation: Phase images contain important information useful in many fields. However, the phase data is often wrapped into a specific range, while background or noise signal in imaging scene may bring significant interference.

Goal(s): To obtain the exact information, phase images need an accurate processing that includes the unwrapping and the background removal.

Approach:  In this paper, we propose a positive and negative learning based image decomposition network (PNnet) to accomplish the phase processing by a single network.

Results:  Experimental results demonstrate that PNnet can achieve excellent performance and efficient generalization, even for complex wrapping and inhomogeneous background.

Impact: Except magnitude images, phase data in MRI also contain important information that is useful in many fields and scenarios. This work proposed a SOTA method for phase processing with high accuracy and excellent performance.  

Keywords: Blood Vessels, Blood vessels, Velocity & flow; Vessel size

Motivation: To address the gap in MRI techniques for assessing cerebral small vessels with slow flow non-invasively.

Goal(s): To develop a quantitative 2D time-of-flight (qTOF) technique for measuring blood velocity and the size of cerebral small vessels.

Approach: We developed an analytic qTOF framework to generate realistic TOF model images, which are optimized to match the acquired TOF images for extracting blood velocity and vessel size.  

Results: The proposed qTOF framework was validated in simulation and phantom studies, and demonstrated in vivo. Incorporating a second acquisition improved blood velocity and vessel size estimation. Flow velocities were comparable to those measured by phase-contrast MRI.

Impact: A quantitative Time-of-Flight technique was developed to provide insights into blood flow and the size of cerebral small vessels, and dynamically in response to changing brain activity, helping to elucidate the role of cerebral small vessels in healthy brain function.

Keywords: Blood Vessels, Blood vessels

Motivation: In vivo imaging of intra-cortical vessels of human brain, including penetrating arteries, veins and capillary density are still scarce.

Goal(s): To reconstruct the in vivo intra-cortical vessels of the human brain and estimate the cortical layer-specific changes in susceptibility (χ) in the presence of superparamagnetic iron oxides.

Approach: With aid of Ferumoxytol at 7T, high resolution gradient echo imaging was implemented to reconstruct pre-/post-SWI, R2* and χ maps.

Results: Intra-cortical penetrating arteries and veins can be differentiated by pre- and post-contrast SWI. Changes in R2* and χ revealed variations reflective of capillary density across different layers, which is in agreement with histological findings.

Impact: This study provides in vivo imaging characterization of intra-cortical vessels of human brain using high-resolution Ferumoxytol-enhanced SWI at 7T. Utilizing changes in R2* and χ enables us delve deeper into the laminar distribution of capillary density across various cortical layers.

Keywords: Inflammation, Infiltration, Heart

Motivation: The prognostic value of T1 mapping and extracellular volume fraction (ECV) in acute myocarditis has not yet been supported by high-quality, evidence-based medicine.

Goal(s): To investigate the prognostic value of T1 mapping and ECV in patients with acute suspected myocarditis.

Approach: Patients meeting the recommended clinical criteria for suspected myocarditis were enrolled.  The potential value for predicting MACE was explored using Cox proportional hazards models.  

Results: Patients with MACE showed higher global native T1 and ECV z scores and were more likely to have tissue changes in interventricular septum. Quantitative mapping parameters have incremental prognostic value beyond clinical variables and conventional CMR parameters.

Impact: Our study reveals the prognostic predictive ability of native T1 and ECV in myocarditis. The application of mapping techniques will further contribute to the understanding of the pathophysiology of heart disease and will guide the development of effective therapeutic approaches.

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Oncology, Cancer, DMG, Diffuse Midline Glioma

Motivation: Pediatric diffuse midline gliomas are associated with a poor prognosis, leaving radiotherapy as standard of palliative care. Personalized radiation regimes could maximize the benefit for the patient, and consequently improve clinical outcomes.

Goal(s): This study explores a state-of-the-art computer vision method to predict the anatomical growth of tumors which could inform tailored radiotherapy treatments.

Approach: A denoising diffusion implicit model is employed to generate realistic, high-quality magnetic resonance imaging scans of enlarged tumor sizes starting from a baseline image.

Results: Our proof-of-concept study demonstrates promising results on an external longitudinal pediatric dataset, highlighting the method’s potential to realistically predict visual tumor growth. 

Impact:  We demonstrate realistic predictions of anatomical (pediatric) brain tumor growth using a generative denoising diffusion implicit model. This enables personalized predictions of tumor growth trajectories to guide localized therapies such as geometric dose shaping for radiotherapy delivery.

Keywords: Peripheral Nerves, Neurography

Motivation: Quantification of myelin in peripheral nerves remains challenging.

Goal(s): To evaluate new imaging techniques that quantify myelin in the peripheral nerve.

Approach:  Myelin-sensitive inversion recovery (MySIR) was compared with inhomogeneous magnetization transfer (ihMT) for its ability to reflect myelin value and spatial resolution in the peripheral nerve.

Results: Myelin values of MySIR and ihMT were strongly correlated. MySIR had a better ability to visualize nerve fascicles than ihMT.

Impact: MySIR could quantify myelin while maintaining high spacial resolution in peripheral nerves.

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, subvoxel QSM

Motivation: Subvoxel QSM could be beneficial for assessing the knee cartilage but requires two separate sequences for reconstruction by using APART-QSM.

Goal(s): To achieve subvoxel QSM reconstruction of knee cartilage in a single scan. 

Approach: A multi-contrast framework was used to simultaneously estimate $$$T_1$$$, $$$T_2$$$ and $$$T_2^{*}$$$ mapping in one scan. The magnitude and phase images were generated based on the signal equation. The preprocessed phase and $$$R_2^{'}$$$(=$$$1/{T_2^*}$$$-$$$1/{T_2}$$$) were used for subvoxel QSM reconstruction. The results were compared with conventional approach using two sequences (GRE+MSE). 

Results: The subvoxel QSM results using the multi-contrast framework have good agreement with the conventional condition. 

Impact: The diamagnetic and paramagnetic susceptibility source separation of the knee cartilage could be achieved in a single scan using a multi-contrast framework. This technique can provide specific information to assess the tissue magnetic properties of the knee cartilage.

Keywords: Visualization, Lung

Motivation: The Pulmonary perfusion defects were unknown in post-acute COVID-19 syndrome (PACS). 

Goal(s): To investigate the utility of phase-resolved functional lung (PREFUL) MRI in detecting pulmonary perfusion disturbances inPACS.

Approach: Participants diagnosed with PACS were recruited, along with healthy (NCT05933317). The quantified parameter QDP derived from PREFUL MRI represents abnormal pulmonary blood flow.

Results: 44 participants with PACS, and 43 healthy were assessed. QDP significantly exceeded  healthy controls in PACS (39.81% ± 15.0% vs 8.2% ± 3.3%) and was notably higher in inpatients (46.8% ± 17.0% vs 34.5% ± 10.8%). Moreover, males exhibited higher QDP than females (43.9% ± 16.8% vs 34.4% ± 10.2%).

Impact: PREFUL MRI demonstrates notable perfusion defects in participants with PACS.

Keywords: Kidney, Kidney

Motivation: Multiparametric MRI of the kidneys is a promising technique for renal diagnostics, but the diversity of imaging protocols and analysis strategies hinders clinical translation.

Goal(s): Our goal was to assess the repeatability of a multiparametric MRI protocol comparing ROI-based and tissue segmentation based analysis.

Approach: Ten volunteers were examined with a multiparametric MRI protocol including ASL, IVIM, BOLD, T1 and T2 mapping twice with one week between visits.

Results: Good repeatability of the multiparametric protocol could be achieved. T1 and T2 values showed less variability compared to perfusion and diffusion related functional parameters. Tissue segmentation showed better repeatability compared to ROI-based analysis.

Impact: Our study demonstrates relatively high repeatability of multiparametric functional MRI of the kidneys. The results of the image analysis methods suggest, that manual segmentation is to be preferred over ROI-based analysis, if automated segmentation is not available.

Keywords: Liver, Liver

Motivation: Accurately predicting microvascular invasion (MVI) risk in hepatocellular carcinoma before surgery could aid clinicians in selecting appropriate surgical approaches to improve the patient’s prognosis.

Goal(s): To construct DCE-MRI based nomogram for predicting MVI, and to assess its ability for stratifying the risk of recurrence after hepatectomy and guiding surgical approaches.

Approach: Quantitative DCE-MRI parameters from both intra-tumoral region (ITR) and peritumoral region (PTR), along with clinical-radiological (CR) features, were utilized to establish the nomogram.

Results: The nomogram presented AUC values of 0.966 in the training and 0.937 in the validation set for predicting MVI. High-risk patients could obtain survival benefit from anatomical resection.

Impact: We constructed and evaluated the performance of the bi-regional quantitative DCE-MRI based nomogram for predicting MVI risk in HCC. Our predictive model effectively predicts MVI risk and assists clinicians in selecting appropriate therapeutic strategies for patients.

Keywords: Cancer, Relaxometry, T1-mapping

Motivation: MRI quantitative technique T1 mapping can reflect the change of intrinsic information of tissues

Goal(s): To explore the value of T1 mapping in PDCA with different degrees of differentiation

Approach: Comparison of PDCA with different degrees of differentiation, the T1 relaxation time before and after enhancement

Results: by  comparing the T1 mapping values before and after enhancement of PDAC with different degrees of differentiation, it was found that as the degree of differentiation of PDAC was getting higher, the Native T1 value showed a tendency to increase. The Native T1 was moderately positively correlated with the degree of pathological differentiation

Impact: T1 mapping may serve as a noninvasive means of predicting the degree of pathologic differentiation of PDAC before surgery.Give clinicians earlier information.

Keywords: Liver, Relaxometry, Iron Overload

Motivation: Estimation of liver iron concentration by R2* relaxation (LIC_R2*) is a powerful and widely used technique, however, it may fail from signal loss at high liver iron concentration.  

Goal(s): To estimate LIC from a single-TE liver-muscle signal intensity ratio (LIC_SIR) and validate at 1.5 and 3.0 Tesla.  

Approach: Using LIC_R2* estimates collected at 1.5T as a reference, we compared LIC_SIR estimates in 15 subjects who had undergone MRI examination at both 1.5T and 3.0T.

Results: We were able to derive field-independent scaling constants that allow LIC_SIR estimation at 1.5 and 3.0T, more than doubling the effective dynamic range of LIC_R2* estimation.

Impact: This generalized framework for LIC_SIR estimation allows reasonable LIC values to be reported (and trended) in patients for whom traditional relaxometry has failed. It also allows approximate LIC calculation from commonly used single and dual echo gradient echo acquisitions.

Keywords: Placenta, Placenta

Motivation: To evaluate virtual magnetic resonance elastography in healthy and preeclamptic(PE) pregnancies.

Goal(s):  To compare the stiffness value (μ_diff)  and apparent diffusion coeffificient (ADC) in healthy and PE pregnancies.

Approach: DWI(b-value of 50 ,200and 800 s/mm² ) were performed on all pregnant women using a 1.5 T MRI scanner. The value of μ_diff  and ADC were calculated and compared between groups.

Results: The mean ADC value of the control and PE groups were 1.414±0.228×10^-3 mm²/s and 1.737±0.107×10^-3 mm²/s, respectively.  The mean  μ_diff value of PE were 5.901±1.757 and 4.618±2.055 kPa, respectively. The area under the curve for μ_diff was 0.903 and ADC was 0.796, respectively. 

Impact: Placental  μdiff value was found to be more reliable than ADC in differentiating between normal and preeclampsia placentas.

Keywords: Cancer, Breast

Motivation: The lack of reliable biomarkers for assessing tumor characteristics and the limitations of histological analysis due to tumor heterogeneity led to the exploration of  diffusion-weighted parameters.

Goal(s): To investigate the association between DW parameters and Ki-67 expression in triple-negative breast cancer, with a focus on whether they can serve as prognostic biomarkers.

Approach: Seventeen triple-negative breast cancer mice with a PDX model underwent 7T MRI scans, yielding DW images. Advanced analysis evaluated ADC and non-Gaussian diffusion parameters, validated through histological Ki-67 staining.

Results: DWI parameters (S-index, sADC, and ADC₀) show strong correlations with Ki67 levels,  at short and long diffusion times (9, 27.6ms).

Impact: Promising prognostic biomarkers for triple-negative breast cancer, DWI parameters (S-index, sADC, and ADC₀) displayed strong correlations with Ki-67 expression, at short and long diffusion times. This validation through accurate DWI-pathology comparison highlights imaging's pivotal role in advancing breast cancer diagnosis.

Keywords: Breast, Cancer, molecular subtypes

Motivation: To analyze the value of imaging examination in accurate diagnosis of breast cancer.

Goal(s): Provide an non-invasive examination for the prediction of molecular subtypes of breast cancer before treatment.

Approach: The quantitative parameters of IVIM-DWI and DCE-MRI in patients with breast cancer without any invasive examination and treatment were compared with pathological molecular subtypes, so as to analyze their diagnostic value in the prediction of molecular subtypes.

Results: DCE-MRI and IVIM-DWI were correlated with immune prognostic factors of breast cancer and had differential diagnostic value for different molecular subtypes.

Impact: DCE-MRI and IVIM-DWI could provide a non-invasive diagnostic method for predicting molecular subtypes of breast cancer and provide a reference for further development of personalized treatment plans.  

Keywords: Software Tools, Cancer

Motivation: Discrimination between true and false positive targets identified using PI-RADSv2 is needed to avoid unnecessary biopsies.

Goal(s): To investigate how quantitative analysis of prostate mpMRI through Habitat Risk Scoring (HRS) combined with PI-RADS can improve prostate lesion identification compared to using PI-RADS alone.

Approach: In prospective clinical trials lesions identified by PI-RADS and/or HRS were targeted through MRI/ultrasound fusion biopsies.

Results: Using HRS yields 100% NPV in the PI-RADS 3 cohort and increases PPV by 7.4% in the PI-RADS 4-5 cohort with respect to clinically significant cancer. Overall, the NPV and PPV increased by 21.4% and 13.2% respectively.

Impact: We present a quantitative imaging approach to complement the current standard for assessing prostate cancer risk in mpMRI data and demonstrate that the use of HRS strengthens fidelity in both positive and negative detections.

Keywords: fMRI Analysis, Diffusion/other diffusion imaging techniques, histogram analysis

Motivation: Currently, perineural invasion (PNI) of rectal cancer (RC) can only be confirmed by pathological examination of postoperative specimens. 

Goal(s): It aimed to investigate the intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in diagnosing the PNI status of RC by histogram analysis.

Approach: We extracted histogram features from 7 parametric maps derived from IVIM-DWI. The independent predictive histogram features of rectal cancer PNI were combined with the percentage of rectal wall
enclosure（PCI ) reported by MRI to construct a combined model for preoperative diagnosis of PNI.

Results: The AUC of the combined model is higher than that of each single-parameter model and histogram model.

Impact: This study demonstrated that full-volume histogram parameters basing on IVIM-DWI can be used to assess PNI status in rectal cancer. Histogram analysis, as a non-invasive tool, may be more valuable not only in rectal cancer research in the future.

Keywords: Treatment Response, Cancer, Multi-Center and Multi-Vendor platform, DCE-MRI, Therapy Response, Ktrans

Motivation: Validate Shutter-Speed model (SSM) DCE-MRI as a robust predictor of breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center and multi-vendor platform setting.

Goal(s): Compare tumor size, semi-quantitative, and quantitative DCE-MRI for early prediction of NAC response.

Approach: BC patients treated with NAC underwent longitudinal high spatiotemporal resolution DCE-MRI at three sites using a 3T Siemens, GE, or Philips system.  Semi-quantitative signal-enhancement-ratio (SER) and quantitative Tofts model (TM) and SSM pharmacokinetic (PK) parameters were derived from DCE time-course data.

Results: PK parameters outperformed size and SER while SSM was superior to TM in early prediction of pathologic response.

Impact: It is feasible to implement quantitative high spatiotemporal resolution SSM DCE-MRI in trials with multi-center and multi-vendor platform settings for robust assessment of BC response to NAC.

Keywords: Other Interventional, Quantitative Imaging

Motivation: Due to the internal heterogeneity of the nasopharyngeal carcinoma, conventional treatment fails to enhance the tumor microenvironment, ultimately leading to recurrence or metastasis.

Goal(s): To investigate whether quantitative values derived from T1, T2, and PD maps can serve as an assessment index for combined immunotherapy with chemotherapy in patients with newly diagnosed locally advanced NPC.

Approach: Synthetic MRI, non-invasive technique, can generate quantitative values for intrinsic tissue features.

Results: The study indicate the potential of PD and T2 values in distinguishing between two treatment. A larger sample size is required to further validate the value of SyMRI in evaluating immunotherapy for NPC.

Impact: Synthetic MRI can generate longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD).

Keywords: Susceptibility/QSM, Quantitative Imaging, Phantoms, QSM, Simulation, Susceptibility, Anisotropy

Motivation: Positive and negative susceptibility mapping is an emerging method that can benefit from the availability of validation tools.

Goal(s): To create an in-silico brain phantom for positive and negative susceptibility and to assess the impact of white matter’s anisotropic susceptibility on susceptibility-separation techniques.

Approach: Simulate positive and negative susceptibility maps and gradient-echo data with/without anisotropy. Process simulated data with different susceptibility-separation algorithms. Compare the results with the ground truth.

Results: The error associated with negative susceptibility measurements is ~9% greater when anisotropy effects are present in the phantom, suggesting that a new susceptibility-separation algorithm that considers myelin’s anisotropic susceptibility may be warranted. 

Impact: Researchers developing novel magnetic susceptibility-separation methods can use our proposed phantom to test different aspects of their technique, ranging from the biophysical model to image processing methods and imaging protocol parameters. 

Keywords: Electromagnetic Tissue Properties, Challenges, Quantitative Susceptibility Mapping

Motivation: Reconstruction challenges for Quantitative Susceptibility Mapping (QSM) offer a common evaluation but are challenging to run and offer only a single snapshot of algorithm performance in time.

Goal(s): To develop a QSM challenge with continuous integration (QSM-CI), enabling automatic, transparent evaluation of community-submitted algorithms across diverse datasets and metrics.

Approach: QSM-CI was implemented using GitHub Actions and a user interface for displaying metrics and collecting community visual ratings, which inform a qualitative Elo metric alongside quantitative assessments.

Results: The QSM-CI prototype implementation is publicly available and has been tested using a range of QSM algorithms.

Impact: QSM-CI will facilitate a QSM challenge that allows for continuous evaluations using current and future datasets, algorithms, and metrics. This ensures the continued accessibility of the challenge and continued relevance as new methods, metrics and test data are made available.

Keywords: Gray Matter, Gray Matter

Motivation: Sickle cell anaemia (SCA) is a major global health burden, but disease mechanisms in the brain are not well understood.

Goal(s): To improve a quantitative susceptibility mapping (QSM) pipeline and apply it to updated data to investigate brain susceptibility differences between SCA patients and controls. To investigate correlations between blood haemoglobin and brain magnetic susceptibility. 

Approach: QSM was optimised using denoising/masking approaches. Linear regressions of susceptibility against log(age) were used to compare age-corrected susceptibility in grey matter structures across the age range and correlate with haemoglobin.

Results: Susceptibility increases with age differently for SCA vs controls. Haemoglobin was not significantly correlated with susceptibility.

Impact: This work provides novel insight into the relationship between grey matter magnetic susceptibility and sickle cell anaemia, demonstrating differential trajectories with age between SCA patients and healthy controls. This may support the view of SCA as an accelerated aging syndrome.

Keywords: Software Tools, Susceptibility

Motivation: The magnetic susceptibility is a fundamental material property for MR and NMR equipment engineering. The literature provides several theoretical solutions to measure the magnetic susceptibility. However, an openly available implementation that allows to determine the magnetic susceptibility automatically is missing.

Goal(s): Develop a non-proprietary approach to determine the magnetic susceptibility from measured field maps.

Approach: Measure the field map of a cylindrical sample. Develop a Python program to extract the magnetic susceptibility of the sample.

Results: The measured reference samples reflect the magnetic susceptibility of the literature. Code for data processing is available through the open access repository.

Impact: Our research provides a programmatic solution to automatically determine the magnetic susceptibility of cylindrical samples from field map measurements in MRI systems. This will aid MR and NMR equipment engineers to measure the magnetic susceptibility of any material of interest.

Keywords: Phantoms, Phantoms, Elastography, Stiffness

Motivation: Quantitative quality assurance (QA) methods for magnetic resonance elastography (MRE) are needed for clinical care but are currently unavailable.

Goal(s): Evaluate stiffness measurement variability from two phantoms of different stiffness using four types of passive drivers: two commercial drivers, a custom stand-style driver, and a novel driver integrated into the phantom housing.

Approach: Stiffness measurements were quantified in ten acquisitions in two phantoms of differing stiffness by four passive drivers. Measurement variability was compared by standard deviations and Whisker-and-Box plots.

Results: The integrated driver achieved the best performance with the highest repeatability, ie: lowest variability, compared to the other drivers.

Impact: An MRE phantom equipped with an integrated passive driver could improve repeatability in MRE quality assurance, potentially enhancing the reliability of QA processes in both clinical trials and patient care.  
 
 

Keywords: High-Field MRI, High-Field MRI, Fat & Fat/Water Separation

Motivation: Nonalcoholic fatty liver disease (NAFLD) is an escalating health issue, necessitating precise noninvasive measurement of hepatic steatosis.

Goal(s): To evaluate the feasibility and accuracy of 5T magnetic resonance spectroscopy (MRS) for in vivo liver fat quantification.

Approach: The study utilized phantoms with controlled fat content and 20 volunteers, comparing proton density fat fraction (PDFF) values measured by 5T MRS against those from 1.5T MRS.

Results: The 5T MRS demonstrated strong consistency with 1.5T measurements, validating its potential in clinical diagnostics despite technical challenges associated with ultra-high-field MRI applications.

Impact: The study's validation of 5T MRS for liver fat quantification could enhance diagnostic precision for liver conditions, influencing clinical practices and guiding future technological advancements in MRI diagnostics.

Keywords: In Silico, In Silico, MRI-based CFD, Urodynamics

Motivation: Catheter-based urodynamic studies to assess bladder dysfunction are invasive and provide inadequate biomechanical information. MRI-based computational fluid dynamics (CFD) has demonstrated potential to uncover these features not evident from catheterization.

Goal(s): Develop and implement a computational methodology to non-invasively assess urodynamics.

Approach: Acquire 3D dynamic MRI of bladder voiding. Use the images to execute subject-specific CFD simulations of the bladder and urethra. Calculate existing urological nomograms and energy expended to quantify bladder function using the MRI and CFD results.

Results: The healthy subject showed unobstructed bladder outlet and normal contractility. We calculated the energy expended to void bladder for the first time.

Impact: A method using MRI-based computational fluid dynamics was developed to simulate bladder voiding. Results show successful quantification of urine flow dynamics. This method shows potential to overcome limitations of current invasive catheter-based urodynamic studies.

Keywords: Whole Body, Body, liver, kidney, parallel transmission, Dixon imaging

Motivation: Improving  the quality of body  R₂*( T₂* ) images  by removing transmit field inhomogeneity while utilizing Dixon imaging in body at 3T.

Goal(s): Implementation of Bilateral Orthogonality Generative Acquisitions method for simultaneously obtaining homogeneous R₂*( T₂* ) and Dixon imaging fro kidney and liver at 3T.

Approach: Bilateral Orthogonality Generative Acquisitions method was improved to include phase effect that enables the use of Dixon Imaging. Multi-echo acquisitions are utilized for R₂* estimation.

Results: Implementation of Bilateral Orthogonality Generative Acquisitions method enables the simulataneous  R₂*( T₂* ) and Dixon imaging and eliminates the central brightnening effect. 

Impact: Simultaneous  homogeneous R₂*( T₂* ) and Dixon imaging is implemented within same scan time required for Dixon imaging while removing the central brightening effect in body imaging at 3T. 

Keywords: In Silico, Quantitative Imaging, Digital twin

Motivation: Prediction of cancer therapy outcomes is a paramount objective in oncology, closely tied to the integration of novel biomarkers into clinical practice.

Goal(s): The goal of creating digital twins of solid tumors is to equip oncologists with a comprehensive replica of the tumor, allowing them to make well-informed decisions.

Approach: In the development of digital twins for solid tumors, we introduce a multidisciplinary approach that essentially combines quantitative MRI and computational modeling.

Results: The image-based model yields a comprehensive representation of tumor perfusion, providing a map of elevated interstitial fluid pressure, which holds significant potential as a biomarker in oncology.

Impact: Cancer therapy's success is not guaranteed, with potential serious side effects. Our aim is to offer a robust digital tumor replica for evaluating numerous treatment options, identifying the optimal plan while minimizing adverse effects.

Keywords: High-Field MRI, Cartilage, Acetabular Labrum, UTE

Motivation: The acetabulum labrum plays a critical role in the hip function. Medical imaging techniques for detecting labral degeneration may help improve our knowledge of its role in hip osteoarthritis (OA).

Goal(s): This study aimed to explore the correlation between the mechanical properties of the acetabulum labrum and MRI properties. 

Approach: The correlations of UTE-T2* and -T1 sequences with the tensile elastic modulus of human acetabular labrum specimens have been investigated.

Results: There was a significant correlation between quantitative UTE-T2*, and UTE-T1 techniques and the mechanical properties of the labrum.

Impact: UTE-T2* and -T1 sequences showed the potential to evaluate acetabulum labrum mechanical assessment, which is needed to improve labrum degeneration detection and monitoring, a primary unmet need in areas where hip osteoarthritis is common and conventional MRI is being implemented.

Keywords: Phantoms, Phantoms, multiparametric

Motivation: Currently there is no qMRI phantom that simultaneously reproduces the many contrasts that are present in white matter.

Goal(s): The goal of this work is to characterize a single multiparametric qMRI phantom that exhibits diffusion, magnetization transfer and MET₂ properties.

Approach: Aqueous MTF and PVP were combined in varying concentrations and evaluated with both NMR and MRI.

Results: A homogenous, multiparametric qMRI phantom was created with an array of tissue relevant parameters: T_2L ranging from 71.9-183 ms, T_2s ranging from 13.6-17.7 ms, f_s ranging from 0.118-0.177, T_1free ranging from 1479-1919 ms and f_m ranging from 0.053-0.109.

Impact: A multiparametric phantom allows for improved quality assurance analysis, as well as sequence development by allowing for multiple contrasts to be evaluated simultaneously. Ultimately providing a sample that is controlled, yet more akin to what occurs in biological samples.

Keywords: Phantoms, Phantoms, Reproducibility, Test-Retest

Motivation: The demonstration of the independence of fBIRN QA metrics on the phantom used would enable an easy cross-scanner comparison of scanner stability, thus improving such QA for clinical use (e.g., presurgical mapping).

Goal(s): The goal of this study was to assess the variance measured using such metrics across phantoms.

Approach: Nine identical phantoms were scanned on a single 3T scanner. Relaxometry and fBIRN scanning was performed on all phantoms and compared to those measured on a single phantom scanned 15 times.

Results: No significant difference was found between phantoms on either their relaxivities, or their QA fBIRN parameters.

Impact: The independence of fBIRN QA metrics on the phantom used found in this work enables the use of generalised QA across MRI scanners to assess their capacity at providing high quality fMRI for presurgical mapping, thereby ensuring optimal patient outcomes.

Keywords: Phantoms, Phantoms, Metrology, Traceability

Motivation: Quantitative MRI is a powerful tool for measuring a variety of biological parameters, with two common biomarkers of interest being fat fraction and Iron content. 

Goal(s): We present here a test object for these parameters which is supported by fundamental metrology and traceable to the SI system. 

Approach: Initial scan data taken at 1.5T is compared with traceable measurements of phantom properties

Results: We see significant variation seen in clinical results of the same phantom even with standardised protocols, outside the range of phantom validation.

Impact: We demonstrate a new gold standard and verified phantom for fat and iron measurement, traceable to primary standards. We present results using standardised MRI protocols which is vital for understanding and improving standards and best practice guidelines in the future.

Keywords: System Imperfections, Spectroscopy

Motivation: Spectroscopy is sensitive to frequency drift. After running sequences that have a high gradient duty-cycle, frequency continues to drift due to gradient cooling.

Goal(s): To propose a generalized prospective real-time frequency correction that can be applied to imaging and spectroscopic sequences, where TR-variant and -invariant events are separable in time.

Approach: Real-time frequency adjustment was implemented for CSI, using a navigator placed where the spin phase is consistent across TR cycles prior to the CSI readout.

Results: Phantom measurements with the generalized navigator provide accurate estimates of the frequency drift, thereby minimizing spectral distortion and providing an improved baseline in the final spectra.

Impact: Prospective real-time frequency adjustment using a generalized navigator approach, where the spin phase is consistent across TR cycles, can be applied to spectroscopic imaging to correct for frequency drift caused by gradient heating and cooling.

Keywords: Neurofluids, Neurofluids, CSF, Velocity & Flow, Clearance, Brain, Neuro

Motivation: Clearance is important for healthy brain functioning. The ability to measure CSF net velocity would be valuable to gain insight into the underlying mechanisms and pathways of clearance.

Goal(s): To measure CSF net velocities in FH and RL direction whilst accounting for periodic motions, involuntary head motion and eddy currents.

Approach: A multi-slice single shot DENSE acquisition is used to measure CSF displacements over time.

Results: The measured net velocity does not fit the classical view on CSF excretion and absorption locations. Further validation is needed using a moving flow phantom.

Impact: The measured net velocities are about 10 percent of what would be expected. If confirmed in a larger cohort, the results challenge the classical view of main CSF excretion at the choroid plexus and absorption at the sagittal sinus.

Keywords: Gradients, Safety, Gradients Optimization, Acoustic Noise, OGSE

Motivation: Oscillating gradient spin-echo (OGSE) diffusion MRI sequence involves diffusion preparation and EPI readout, both having rapidly switching trapezoid gradient. Consequently, OGSE sequence generates strong acoustic noise that may introduce comfortless patient experience.

Goal(s): Our study aims to develop an optimization framework to suppress the acoustic noise by softening the trapezoid gradient.

Approach: We measured the acoustic noise frequency response function of scanner. Based on linear model of MRI acoustic noise generation, we designed a convex optimization framework to reduce the predicted A-weighting sound pressure level(SPL) 

Results: Optimized gradient achieved 14.09dBA SPL reduction according to our FRF based prediction.

Impact: We developed an effective optimization method to reduce the acoustic noise of oscillating trapezoid gradient waveform, and potentially facilitate the clinical applications of OGSE. This optimization framework also has potential to reduce acoustic noise of EPI readout waveform

Keywords: High-Field MRI, Metabolism, spectroscopy

Motivation: Proton short echo (TE≤10ms) MR spectroscopy of human brain at high magnetic fields (≥3T) provides abundant metabolic information beyond MR images, but remains challenging for routine clinical usages beyond 3T.

Goal(s): To evaluate feasibility and assess quality of proton ultrashort echo (i.e.10ms) MR spectroscopy of human brain on a clinical, whole-body 5T MRI system.

Approach: Stimulated echo acquisition mode (STEAM) spectra with TE=10ms were obtained in human brain at 3T and 5T.

Results: The feasibility of short echo MR spectroscopy of human brain at 5T was first demonstrated. The spectral quality is substantially improved when compared to 3T.

Impact: The quality of short echo MR spectra at 5T could be reached without exceeding the SAR and thus may offer additional metabolite information for large amount of clinical diagnostic applications.

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation: The use of different imaging tools at various hospitals results in varying contrast images. This fragmentation in healthcare prompted me to develop a personalized network that can be trained using hospital imaging database.

Goal(s): The main goal of this project is to predict early stages of Alzheimer's Disease (AD) using Magnetic Resonance (MR) images.

Approach: We applied convolutional neural network (CNN) to the T1 weighted images of AD, publicly available at https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images. The images were classified into four classes. F1 score and Area Under Curve (AUC) were calculated for the model after training.

Results: We demonstrated F1 score of 99.60% and AUC 0.994.

Impact: This model could be used to predict AD to other datasets that might help early detection of AD and subsequently improve treatment strategies. With various mice brain scan training, this network can also be used to aid AD researchers.

Keywords: Diagnosis/Prediction, Radiomics

Motivation: Deep learning forpredicting biochemical recurrence (BCR) is feasible but needs further evaluation in advanced prostate cancer (PCa). 

Goal(s): We aimed to develop radiomics models with automatic segmentation derived from pretreatment ADC maps that may be predictive of BCR in advanced PCa.

Approach: In this study, PCa areas were segmented on ADC images by using a pre-trained artificial intelligence (AI) model. Three models were constructed to evaluate BCR prediction level.

Results: The deep-radiomics model was superior than the clinical model and the conventional radiomics model in the aspect of prediction accuracy, clinical impact and risk assessment.

Impact: With accurate BCR prediction by deep-radiomics model, more appropriate treatment plans may be formulated and intervention treatment can be carried out as soon as possible, resulting in better prognosis for patients with PCa.

Keywords: AI/ML Software, Fat

Motivation: Currently there is a widely validated commercial semi-automatic method called AMRA® Researcher, which quantifies ASAT and VAT. However, it is not accessible to everyone due to the necessary economic means.

Goal(s): To develop an automatic, simple and free methodology to quantify ASAT and VAT, with at least the same precision as AMRA® Researcher.

Approach: Preprocessing and simple CNNs applied on in-phase Dixon MRI sequences were proposed for quantify VAT and ASAT.

Results: There were no significant differences between the quantifications from AMRA Researcher and our methodology. Both obtained a high correlation and our methodology reached the precision of AMRA® Researcher.

Impact: Our automatic, simple and free ASAT and VAT quantification methodology, studying MRI through preprocessing and CNNs, achieved the precision of the commercial semi-automatic AMRA Researcher method. After future independent validation, this could become an accessible tool to assist specialists.

Keywords: Diagnosis/Prediction, Diffusion/other diffusion imaging techniques, Brain, gray matter, white matter

Motivation: Bottom-up energy budgets provide a way to quantify electrical activity in the brain using metabolic imaging. However, existing models are not patient-specific, instead using generalized neural cell counts, preventing direct measures of cognitive activity in the brain.

Goal(s): Our goal was to use a convolutional neural network (CNN) to demonstrate the possibility of predicting individualized neural cell counts.

Approach: Multi-modal MRI from nine patients was used to model neural and synaptic density predictions, which were compared to silver standard counts using correlation coefficient in a cross-validation study.

Results: The model demonstrates an ability to predict patient-specific energy budgets.

Impact: The success of machine learning methods in predicting neural cell and synaptic density paves the way for the use of CNNs to generate patient-specific energy budgets, improving understanding of brain energetics at a microscopic level in health and disease.

Keywords: AI/ML Software, Segmentation

Motivation: 3D visualisation of the spinal cord and vertebrae anatomy is critical for treatment planning and assessment of cord atrophy in neurodegenerative and traumatic diseases.

Goal(s): Develop a fully automatic segmentation of the whole spinal cord, vertebrae and discs.

Approach: The hybrid method combines a nnU-Net with an iterative processing algorithm with Spinal Cord Toolbox to conveniently generate ground truth labels. We used 3D T1w and T2w scans from three different databases.

Results: A validation Dice score of 0.928 was obtained (averaged across contrasts, classes and datasets), suggesting promising segmentation accuracy and capabilities for generalisation given the use of multi-site/multi-vendor datasets.

Impact: The fully automatic segmentation of the spine and spinal cord will pinpoint pathologies at specific vertebrae level, offering visualization for surgery preparation. This could also refine segmentation of substructures like multiple sclerosis lesions and tumors, inspiring solutions for related issues.

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation:  Radiopathomic mapping of glioma could improve standard of care by helping guide surgical resection and subsequent treatment. Most current methods for predicting tumor pathology using MRI neglect intra-tumoral heterogeneity.

Goal(s): We aim to use multi-parametric MRI and deep learning to spatially map pathology for treatment naïve glioma.

Approach: We utilized histopathologically analyzed tissue samples taken during surgical resection with known coordinates on pre-surgical multi-parametric MRI and semi-supervised ensemble networks.

Results: Our model classifies Ki-67 with an AUROC of 0.84 and 0.73 for combined Ki-67 and percent cancerous cells. Including physiologic MRI and pretraining on patches of unknown pathology improved performance.

Impact: We performed radiopathomic mapping in patients with newly-diagnosed glioma using presurgical physiological + anatomical MRI and semi-supervised ensemble networks and achieved AUROCs of 0.84 and 0.73 for Ki-67 and combined Ki-67 and % cancerous cells, respectively.

Keywords: Diagnosis/Prediction, Arterial spin labelling, Moyamoya disease

Motivation: Cognitive function in adult patients with moyamoya disease (MMD) is often impaired because of low cerebral perfusion.

Goal(s): To identify brain regions where low CBF is associated with cognitive dysfunction and assess the predictive performance of radiomics models for cognitive dysfunction in adults MMD.

Approach: 3D-pCASL and logistic regression analysis was employed to quantify CBF and explore independent predictors for preoperative cognitive dysfunction. And five different classifiers were used to establish radiomics models.

Results: Cerebral perfusion in the left LOFL, left IPL, left SMA, and left ACG showed significant associations with cognitive impairment. The final combined model had the best predictive performance.

Impact: Hypoperfusion on 3D-pCASL plays a crucial role in the detection of early cognitive impairment in adults with MMD, and the combined model that combined with CBF and radiomics features of specific brain regions showed better performance in predicting cognitive dysfunction.

Keywords: Diagnosis/Prediction, Segmentation

Motivation: Detection of contrast-enhanced lesions (CELs) is fundamental for the diagnosis and monitoring of Multiple Sclerosis (MS) patients. This task is time-consuming and variable in the clinical setting. However, only a few studies reported automatic approaches.

Goal(s): To develop a deep-learning tool to automatically detect and segment CELs in clinical MRI scans from MS patients.

Approach: We implemented a UNet-based network with an adapted sampling strategy to overcome the scarcity of CELs. We considered the data imbalance to weight the training loss function.

Results: The model performance was evaluated for different lesion-volume ranges and achieved high performance even in low-volume lesions. 

Impact: We developed a deep-learning method fulfilling clinical needs in detecting and segmenting lesions characterized by low volume, low numbers per patient and heterogeneous shapes.

Keywords: Other AI/ML, Data Analysis, Modelling

Motivation: Importance of analyzing deformation fields derived from both intra- and inter-individual pairs of T1-weighted images which could offer insights into typical and atypical  neurodevelopment.

Goal(s): We aimed to fine-tune a 3D CNN to classify intra and inter-individual variability based on log Jacobian maps from deformation fields of pediatric longitudinal MRI.

Approach: 279 log Jacobian maps of both intra- and inter-individual pairs are extracted using ANTs. A 3D CNN is trained in two ways (overlap and no overlap) for binary classification using 10-fold cross-validation.

Results: As expected, the overlap scenario had higher accuracy and F1 score compared to no-overlap, nonetheless both achieving good results.

Impact: This project's focus on pediatric MRI scans aims to understand deformations in medical imaging, advancing diagnostic tools. By distinguishing intra and inter-individual variability using log Jacobian-derived deformation patterns, it subsequently aims to model typical neurodevelopment through trajectories for deviation prediction.

Keywords: AI/ML Image Reconstruction, Cardiovascular

Motivation: To precisely screen out infarction in patients with unrecognized myocardial infarction (UMI) in hope of early intervention to reduce adverse cardiac events.

Goal(s): To evaluate deep learning reconstruction based late gadolinium enhancement (LGE_DL) in comparison with conventional reconstructed LGE (LGE_O) and also to explore an appropriate threshold method for LGE measurements.

Approach: LGE_DL and LGE_O of 77 patients diagnosed with UMI were evaluated for image quality and analyzed for MI areas using different standard deviation thresholds and a full-width-half-height (FWHM) method.

Results: The STRM ≥ 4SD and ≥ 3SD is respectively reckoned as the best reference threshold for LGE_DL and LGE_O.

Impact: The deep-learning based reconstruction LGE images had better image quality and reliable pathological evidences for detection of UMI. Significantly different Parea using threshold techniques for LGE_DL and LGE_O indicated the utility of STRM should be concerned.

Keywords: Diagnosis/Prediction, Brain, Cerebral Palsy

Motivation: Early prediction of cerebral palsy (CP) in infants plays a pivotal role in facilitating tailored rehabilitation treatment.

Goal(s): We hope to achieve early prediction of CP in infants aged 6 months to 2 years old based on MRI and deep learning technology.

Approach: We introduce a novel neural network model, known as the "Cerebral Palsy Brain Constraint Residual Network" (CPBC-Resnet), for the automatic prediction of CP risk based on MRI data.

Results: The CPBC-Resnet model exhibits an impressive receiver operating characteristic area under the curve (AUC) of 0.9521, achieving a sensitivity of 94.12% and a specificity of 100%.

Impact: This study streamlines cerebral palsy (CP) imaging diagnostics, reducing physician training costs, and expanding the reach of CP diagnostic technology. It promotes early CP diagnosis and intervention, particularly in areas with underdeveloped medical standards, contributing to overall child health improvement.

Keywords: Diagnosis/Prediction, PET/MR, Artificial Intelligence,Brain

Motivation: At present, the multi-modal fusion image has the problems of weak functional information performance and much noise.

Goal(s): Based on the existing technology, this research increases the retention of functional information and improves the display quality of the fused image.

Approach: In this study, a multi-scale pyramid convolutional neural network model based on residual structure is constructed, which can extract deeper semantic information while retaining shallow context information.

Results: By constructing a new convolutional neural network, the loss of functional information in the fused image is reduced, the noise of the fused image is reduced, and the image quality is improved.

Impact: The multimodal image fusion technology proposed in this paper preserves the texture information of MRI and CT, and the functional information of PET/SPECT at the same time, which makes more dimensions available for clinical diagnosis in the future.

Keywords: Diagnosis/Prediction, Brain, Volumetric Attention Network, Deep Learning, Astrocytomas, Glioma, Classification

Motivation: Diagnosis and grading of astrocytomas tumour present considerable challenges. Manual grading is time-consuming and error prone. Preoperative MRIs are a useful, yet deep learning presents challenges due to computing limitations and complex architecture.
 

Goal(s): Study introduces novel multimodal MRI classification for grade II and III astrocytomas, aiming to improve accuracy, reduce complexity, and address interclass homogeneity via attention mechanism.

Approach: Single slice from eight MRI modalities forms a three-dimensional cube. Normalized, iPCA processed, and passed to deep model with volumetric attention network.

Results: The DVA using advanced and traditional MRI information outperforms existing models achieving an overall accuracy of 77% using five-fold cross-validation.

Impact: The proposed multimodal MRI classification approach enhances astrocytoma diagnosis and grading. The deep volumetric attention model improves accuracy, reduces model complexity, and holds potential for trustworthiness impacts in clinical practice.

Keywords: Other AI/ML, Machine Learning/Artificial Intelligence

Motivation: A fully automatic workflow for scan plane prescription is desirable in clinical settings.

Goal(s): Our goal is to demonstrate a deep learning-based MRI scan workflow for fully automated MR scanning in the prostate.

Approach: This new scan workflow will identify anatomical landmarks and scan planes for prostate planning (coverage, FOV and orientation) from coil sensitivity and 3plane scout images. 

Results: The deep learning-based anatomy recognition showed acceptable average location error below 5mm and plane orientation error below 10 degrees.

Impact: As no interaction from the operator is required to complete a full MR prostate scan, it paves the way for fully automated MR scans for the prostate anatomy.

Keywords: AI/ML Image Reconstruction, Quantitative Imaging, Fat-water seperation, PDFF, Deep Learning, Fat Quantification, Physics Informed Deep Learning, Synthetic MRI

Motivation: Deep Learning (DL) models have recently been used for fat-water separation in Multi-Echo MRI (ME-MRI). However, DL models may not always be robust and under-perform when not trained with a large and diverse dataset.

Goal(s): This research proposes high-variability synthetic ME-MRI generated using the biophysical model of fat-water separation as a tool for testing the generalizability and robustness of DL-based fat-water separation models.

Approach: High-variability synthetic ME-MRI was used to evaluate the robustness of the recent state-of-the-art DL-based Ad-Hoc Reconstruction (AHR) method for fat-water separation.

Results: The AHR method lacked robustness and synthetic ME-MRIs can be effectively used to test DL models.

Impact: The fat-water maps obtained by processing the Multi Echo-MRI (ME-MRI) are of diagnostic and prognostic value in many diseases. This study investigates the role of synthetic ME-MRIs with high variability in testing the robustness of Deep Learning-based fat-water separation models.

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Segmentation, Urodynamics

Motivation: Bladder dysfunction is assessed using catheterization which are invasive and provides insufficient biomechanical information. MRI urodynamics is tedious because segmentation of bladders over numerous time steps during voiding.

Goal(s): Implement automated segmentation using deep learning for accelerating the workflow of MRI-based urodynamic assessment.

Approach: Train a U-Net using 3D dynamic images and manually segmented masks. Use time and dice score to assess the performance of the network.

Results: Images of bladder voiding from five subjects were used to train the network and can segment one bladder in <3 minutes, compared to 20 minutes for manual segmentation. Dice score was 0.99 showing excellent performance.

Impact: Urodynamic assessment using MRI is a tedious process due to segmentation of the bladder from 3D dynamic image datasets. We automated segmentation using deep learning to accelerate the workflow. Our automated process reduced time sixfold and produces excellent segmentation.

Keywords: Diagnosis/Prediction, Segmentation

Motivation: Pancreatic diseases often exhibit spatial non-uniformity. Achieving automated segmentation of different pancreatic regions and conducting quantitative calculations of volume and fat content can effectively assist physicians in diagnosis and treatment.

Goal(s): Developing a segmentation network to achieve the automatic segmentation of the pancreas and to perform quantitative calculations of volume and fat content in diffrent regions.

Approach: Sample acquisition was performed using Dixon sequences, and training was conducted using an improved nnUnet network. Additionally, an automated pancreatic segmentation and quantitative calculation method was developed.

Results: With a training dataset consisting of 800 cases, the network achieved a segmentation Dice coefficient of 0.92.

Impact: To save professional physician annotation time for early detection and diagnosis of pancreatic diseases, as well as for quantifying changes before and after pancreatic treatments, and to assist in clinical drug therapy.

Keywords: AI/ML Software, Machine Learning/Artificial Intelligence, Natural Language Processing

Motivation: Cohesive multicenter imaging datasets are critical for research, yet variability across institutions poses a significant challenge, especially when aggregating retrospective data for longitudinal disease monitoring.

Goal(s): Here, we present a method for harmonizing multicenter data that produces consistent series descriptions and enhances brain alignment between longitudinal time points.

Approach: We employed an NLP pipeline to standardize series descriptions and an automated algorithm to realign images. We applied these tools to ADNI imaging collected across multiple sites, scanners, and time points.

Results: The pipeline consolidated 101 unique series descriptions into 17 standardized descriptions. The alignment algorithm reduced orientation error and improved longitudinal image consistency.

Impact: Our methodology can impact clinical workflows by streamlining multicenter data analysis and enhancing longitudinal disease monitoring. These techniques improve image consistency between time points, which can facilitate disease monitoring and allow radiologists to assess changes in chronic disorders.

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence

Motivation: Clinical MRI datasets are not always comprehensive or consistent, limiting their use for secondary analysis.

Goal(s): Investigating the suitability of a deep learning model named CycleGAN, with optional spectral normalization, for dealing with the missing sequence problems in clinical imaging as seen in multiple sclerosis (MS). 

Approach: Using standard brain MRI of 104 MS people, we implemented 2 CycleGAN models, one with and one without spectral normalization to compare.

Results:  CycleGAN performed competitively in image transformation between T1-weighted and T2-weighted images. Adding spectral normalization appears to improve performance, especially when the quality of training scans is inconsistent.

Impact: CycleGAN-based model has the potential to generate non-acquired images not always needed in standard clinical imaging, as seen in brain MRI in MS, where the resulting images can help promote various secondary analysis studies including machine learning.

Keywords: Perfusion, Perfusion

Motivation: DSC is the leading methodology for MR-based perfusion imaging. However, the technique's reliance on invasive gadolinium injections poses a major limitation.

Goal(s): Can breath-holding induce perfusion contrast that is exploitable using DSC MRI?

Approach: Ten healthy subjects underwent MRI at both 3T and 7T, while performing eight 16 s breath-holds. Breath-hold-induced signal changes were fed into a DSC MRI analysis pipeline, and perfusion was quantified.

Results: Calculated cerebral perfusion values were within the physiological range of literature values; the breath-hold task yielded significantly higher contrast-to-noise and GM-to-WM contrast with higher field strength and increased scan time, although this plateaued at roughly 6 min.

Impact: For the first time, we show that DSC-MRI using breath-holding allows for the quantification of perfusion parameters. This may have broad implications for neurovascular disease, either circumventing the need for invasive gadolinium injections or shedding additional light into pathology.

Keywords: Arterial Spin Labelling, Pulse Sequence Design

Motivation: Comprehensively characterizing cerebrovascular events including dynamic blood flow patterns and downstream perfusion is important in clinical diagnosis of cerebrovascular disorders.

Goal(s): To develop a time-efficient phase-sensitive ASL technique (PS-ASL) that provides high-quality time-resolved 4D-MRA and perfusion imaging within single scan.

Approach: PS-ASL sequence was designed by combining pCASL and PASL preparations with stack-of-stars golden-angle radial acquisition and a self-constraint low-rank subspace reconstruction. The label and control pulse modules alternated between pCASL and PASL preparations in each TR. Phase-sensitive subtraction between control and label yields 4D-MRA from PASL and perfusion from pCASL.

Results: Both dynamic 4D-MRA and perfusion maps were successfully obtained by PS-ASL.

Impact: The developed PS-ASL technique could be a potential powerful imaging tool in clinical applications, which provides detailed characterization of blood flow from both arterial and capillary beds in a single sequence.

Keywords: Neurofluids, Neurofluids

Motivation: The difference in mLVs outflow between healthy control (HC) and brain disorder patients may contribute to toxic protein aggregation and cognitive decline.

Goal(s): To demonstrate mLVs structure and flow information of brain disorder patients and compare quantitative flow metrics between HC and patient

Approach: We applied IR-ALADDIN to acquire mLVs images around superior sagittal sinus in 20 HCs and 9 patients with various brain disorders such as meningioma, hydrocephalus.  

Results: The mLVs size was reduced in patients compared to HC with no significant difference in mLVs velocity, leading to overall mLVs flow reduction in patients.

Impact: The reduced mLVs flow in brain disorder patients was confirmed using a non-invasive technique. This suggests the possibility to study relationship between brain disorders and waste clearance through the mLVs flow in further studies.

Keywords: Contrast Agents, Perfusion, brain

Motivation: Provide direct measurements of cerebral perfusion metrics, relative cerebral blood flow and volume, and mean transit time.

Goal(s):  Generate a known step susceptibility contrast input rather than requiring back calculation of an arterial input function 

Approach: We used a step reoxygenation of previously deoxygenated lung alveoli to induce a step increase in oxyhemoglobin in arterial blood and analyzed the T2*-weighted signal for each voxel. Perfusion metrics from step deoxyhemoglobin changes were compared to those from conventional analysis using a gadolinium contrast agent in healthy volunteers.  

Results: The perfusion metrics from the step deoxyhemoglobin method were similar to those from Gadolinium injection.  

Impact: Perfusion metrics can be measured directly from a non-invasive test using a step decrease in deoxyhemoglobin generated by instantaneous reoxygenation from a brief hypoxia. They correspond to those calculated indirectly from an intravenously injected Gadolinium contrast agent involving complex analysis.  

Keywords: Arterial Spin Labelling, Vessels

Motivation: T2-relaxation-under-spin-tagging(TRUST) is a non-invasive MRI technique to estimate blood oxygenation in the superior sagittal sinus from a T2 decay fit.

Goal(s):  The calibration curve to convert T2 into oxygenation has been derived with ex-vivo experiments for healthy volunteers and patients with specific types of sickle cell disease.  

Approach: Validation of these calibration curves is difficult because blood cannot be drawn from the superior sagittal sinus itself, hampering implementation of the technique in other patient groups. 
 

Results: Here, TRUST was adapted and optimized for the arm so that its measurements of venous oxygenation can be directly compared with venipunctures from the cephalic vein.

Impact: The TRUST-sequence was optimized for non-invasive T2-measurement in the upper arm veins. A posteriori correlation with blood properties returns a venous oxygenation mapping. This may confirm the currently used calibration curve and facilitate obtaining accurate calibration curves for specific diseases.

Keywords: Hybrid Imaging, Molecular Imaging, Optoacoustic (Photoacoustic) Imaging

Motivation: Interpreting BOLD signals in fMRI studies is a challenging task due to their dependence on multiple factors.

Goal(s): Multiparametric readouts from a recently developed hybrid magnetic resonance optoacoustic tomography system can offer unprecedented capabilities for studying the rodent brain in resting-state conditions.

Approach: Simultaneously acquired multimodal functional data of mice were utilized to link hemoglobin resolved optoacoustic readouts to BOLD.

Results: Multiparametric brain-wide activity were detected under resting-state condition for the first time. Revealed functional connectivity maps and brain networks showed high spatial overlap among the hemodynamic components. BOLD and total hemoglobin functional connectivity were found tightly correlated, compared to oxygenated- and deoxygenated-hemoglobin.

Impact: Hybridization of optoacoustic tomography and MRI is a powerful multi-modal approach due to highly complementary contrasts and capabilities for functional neuroimaging. The more comprehensive functional readings provide unique capabilities for studying neurovascular and neurometabolic coupling mechanisms and related diseases.

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Cerebral blood flow (CBF) is a fundamental physiological measure indicating regional brain function and vascular conditions via arterial spin labeling (ASL) perfusion MRI, but ASL sequences is limited.

Goal(s): Blood-oxygen-level-dependent (BOLD) fMRI is known to be a function of CBF and other physiological sources. We try to extract CBF information from BOLD signal.

Approach: We proposed a convolutional neural network to extract CBF from BOLD fMRI signal.

Results: We confirmed the possibility of using supervised deep learning model to extract CBF from BOLD fMRI from independent sequences.

Impact: Deep learning enables the estimation of CBF signal directly from the prevalent BOLD fMRI images, offering an alternative to the ASL sequence that is not universally available across research facilities.

Keywords: Arterial Spin Labelling, Arterial spin labelling, Challenge; multi-PLD ASL

Motivation: The OSIPI ASL Challenge is a community initiative motivated by open science principles that aim to establish good practices in ASL image analysis and Cerebral Blood Flow (CBF) quantification.

Goal(s): The second ASL challenge's main goal is to provide a thorough comparison of existing post-processing pipelines focusing on Multi-PLD methodology.

Approach: The second roadmap will provide different datasets; a population dataset which will bring real variability to the challenge and synthetic data which allows straightforward ground truth comparison.

Results: The second edition of the ASL Challenge will contribute to gaining new insights into the potential sources of variability within the multi-PLD analysis pipeline.

Impact: Through the second edition of the ASL Challenge, we seek to enhance our understanding of multi-PLD analysis in the ASL community. Its success could establish a consensus on the processing of multi-PLD ASL data, positively influencing clinical and scientific practices.

Keywords: Perfusion, Quantitative Imaging, CBF, perfusion, quantification, flow-related enhancement, pCASL, imaging speed optimization

Motivation: Flow-related enhancement (FREE)-MRI could be used to generate phase-resolved perfusion-weighted brain maps.

Goal(s): To test cerebral blood flow (CBF) estimation using the pulse wave amplitude in FREE-MRI. Secondly, the potential for acceleration was evaluated retrospectively.

Approach: Twenty-four healthy subjects had cerebral MRI with balanced steady-state free precession imaging (FREE-MRI) and with pCASL-MRI for comparison.

Results: The value distribution of the estimated CBF showed disparity of the values between both techniques in the histogram. A Kolmogorov-Smirnov test confirmed differing probability distributions.
The approximated CBF from FREE-MRI remained stable until 50% of the data was reconstructed and reveals large potential acceleration. 

Impact: The proposed technique allows a rough approximation of the cerebral blood flow. Further sequence optimization must be achieved to improve the measurement of lowly perfused tissues. Nevertheless, the technique offers large potential for imaging speed optimization.

Keywords: Velocity/Flow, Velocity & Flow

Motivation:  4D flow imaging in the liver suffers from long acquisition time and inefficient motion control.

Goal(s): Address the challenges of prolonged acquisition time and significant motion artifacts in hepatic 4D flow imaging.

Approach: We collected data from all respiratory states without a diaphragm navigator, and retrospectively reconstructed respiratory-resolved 5D flow using simultaneously recorded breathing signal.

Results: This scheme reduced the acquisition time from over 10 minutes to 6-8 minutes, while maintaining consistent image quality with few motion artifacts. High quantitative correlation in hepatic hemodynamic results was found between the results from prospectively navigated data and retrospectively binned data.

Impact: The proposed hepatic 5D flow scheme achieved high image quality and quantitative metrics, compared with those of diaphragm navigated results. This scheme can effectively shorten the acquisition time to 6-8 minutes and mitigate motion artifacts.

Keywords: Contrast Agents, Blood vessels, Time of flight magnetic resonance venography, Iliac vein compression syndrome

Motivation: The use of respiratory-gated TOF-MRV in assessing the degree of iliac vein compression is easily affected by respiratory uniformity, leading to vascular distortion and corresponding false images.

Goal(s): This study evaluated the diagnostic effectiveness of an improved TOF-MRV (mTOF-MRV) in  Iliac vein compression syndrome (IVCS) diagnosis by optimizing key parameters, compared with conventional TOF-MRV.

Approach: Objectively evaluated the deviation of vascular stenosis degree between TOF MRV and mTOF MRV using DSA as the standard.

Results: The mTOF-MRV has better image quality and can accurately detect venous stenosis. It can be applied in the diagnosis of IVCS and further assessments after endovascular interventions.

Impact: mTOF-MRV more accurately located iliac vein stenosis without obvious stair-step artifacts and magnetic sensitivity artifacts, thereby meeting the diagnostic and clinical requirements It can be applied in the diagnosis of iliac vein compression syndrome and further assessments after endovascular interventions.

Keywords: Multi-Contrast, Brain, Myelin plasticity, diffusion MRI, DTI, corticospinal tract, hippocampus, cerebellum, sensory motor system

Motivation:  MRI studies have demonstrated plastic changes in grey and white matter (GM/WM) during motor skill learning. However, diffusion magnetic resonance imaging (dMRI) might provide additional complementary information in contrast to multi-parametric mapping (MPM), which has been investigated previously. 

Goal(s): To investigate training-induced neuroplasticity using dMRI and contextualize them to the MPM findings.

Approach: Acquisition of longitudinal dMRI and MPM during motor skill learning.

Results: We observed overlapping changes in dMRI and MPM metrics following motor skill learning. dMRI was thereby able to capture additional changes within the WM, whereas within the GM, some findings were unique to the MPM protocol.

Impact: dMRI and MPM metrics are sensitive to motor skill learning-induced changes in GM and WM. To combine the two methodologies advances our capability in detecting neuroplasticity changes and might be beneficial for patient rehabilitation.

Keywords: Perfusion, DSC & DCE Perfusion, Head & Neck Imaging

Motivation: To use fluid-mechanics based deep learning method to predict perfusion parameters from dynamic images

Goal(s): We propose to explore the possibility to use neural network trained on simulated data from fluid mechanics simulation to analyze dynamic medical images.

Approach: We use quantitative transport mapping network (QTMnet), which is trained on simulated concentration propagation profile generated from constrained constructive optimization (CCO)  and transport equation-based tracer propagation simulation, to predict perfusion parameters including flow rate, permeability, vasculature volume, from DCE MRI images.

Results: QTMnet predict perfusion parameters accurately in simulation study and can distinguish different gene expression group patients comparing with using traditional kinetics model.

Impact: Proposed QTMnet method can be used to predict different perfusion parameters related to dynamic images accurately and automatically without usage of arterial input function.

Keywords: Arterial Spin Labelling, Arterial spin labelling, Perfusion, Myocardium, T1 Mapping, T2 Mapping, Reproducibility

Motivation: Contrast-agent free techniques are needed in cardiac MRI studies for assessment of patients with coronary artery disease but with contraindications to gadolinium contrast-agents, such as kidney disease.

Goal(s): The aim of this work was to assess the intra and intersession reproducibility of a multiparametric protocol including three non-contrast techniques (ASL, native T1 and T2 mapping) in healthy controls with a wide age range to eventually test the technique in clinical patients.

Approach: Multiparametric non-contrast cardiac MRI in ten healthy controls at 1.5T.

Results: This protocol can offer contrast free alternative for perfusion quantification, T1 and T2 mapping, with good intra and intersession reproducibility.

Impact: Multiparametric cardiac MRI with arterial spin labelling (ASL) perfusion, T1 and T2 mapping could offer a contrast-agent free alternative for patients with kidney disease, or other contraindication to gadolinium agents, with good intra and intersession reproducibility.

Keywords: Arterial Spin Labelling, COVID-19

Motivation: A proportion of patients admitted to hospital with SARS-CoV-2 infection have cognitive deficits that persist for several months. However, the mechanisms behind persistent neurological symptoms are unclear. Blood-brain barrier (BBB) dysfunction is a possible underlying cause.

Goal(s): We aimed to investigate BBB permeability in participants previously admitted to hospital with SARS-CoV-2 infection (post-acute COVID-19).

Approach: We used a diffusion-prepared pseudo-continuous arterial spin labelling (DP-pCASL) to quantify water exchange rate in post-acute COVID-19 participants and controls.

Results: Post-acute COVID-19 participants demonstrated lower whole brain water exchange rates across the BBB than controls, but no differences in arterial transit time or cerebral blood flow.

Impact: This is the first study to report reduced water exchange across the blood brain barrier in the context of post-acute infection with SARS-CoV-2. This may implicate glymphatic system dysfunction as a mechanism for neurological symptoms associated with long COVID-19.

Keywords: Arterial Spin Labelling, Arterial spin labelling, Simulation

Motivation: We identified a gap in understanding multi-tag Time-SLIP MRI dynamics, crucial for advanced perfusion analysis, particularly in varying physiological and pathological states.

Goal(s): Our goal was to develop a comprehensive simulation tool to accurately predict signal changes in multi-tag Time-SLIP ASL MRI across diverse flow conditions.

Approach: We employed MATLAB to simulate numerically non-selective and  selective multi-tag Time-SLIP pulse sequences, applying Bloch equations to model tissue-specific magnetization and blood flow perfusion dynamics.

Results: Simulations showed distinct magnetization patterns between odd and even pulse tags, aligning with physiological flow rates.

Impact: This simulation tool enables researchers to customize Time-SLIP MRI protocols for detailed study of fluid dynamics across the body, from blood to CSF flow. It provides a solid foundation for developing more comprehensive ASL-related simulations.

Keywords: Arterial Spin Labelling, Perfusion, Pediatrics

Motivation: Moyamoya is a progressive cerebrovascular disorder affecting hemodynamics and the disease often begins during childhood. However, the non-invasive assessments of hemodynamics in pediatric Moyamoya patients remain an open question.

Goal(s): Investigate the CBF change before and after revascularization in pediatric Moyamoya patients.

Approach: CBF in 42 patients was measured using arterial spin labeling on a 3T MRI 1 week before and within 2 weeks after revascularization. CBF in regions affected by vasculopathy was compared before and after revascularization.

Results: CBF in regions affected by vasculopathy increased significantly after revascularization.

Impact: ASL can be utilized to evaluate CBF in pediatric Moyamoya patients before and after direct revascularization.

Keywords: Perfusion, Contrast Mechanisms, breast tumor; Ultrafast Breast MRI

Motivation: Ultrafast (UF)-DCE MRI is a novel approach to obtain kinetic information very early after enhancement, which promises improved capabilities in differentiating malignant from benign lesions of breast cancers.

Goal(s): The early hemodynamic characteristics of lesions using breast UF-DCE MRI were systematically investigated to evaluate the value of the relevant kinetic parameters in the diagnosis of different grades of breast cancer.

Approach: The kinetic parameters of UF-DCE MRI were calculated to differentiate the different grades of breast cancer. 

Results: The kinetic parameters of UF-DCE MRI can be used to distinguish high-grade from benign and low-grade breast cancer.

Impact: Breast ultrafast DCE MRI was used to explore the early hemodynamic characteristics of the lesions and evaluate the value of the relevant dynamic parameters in the diagnosis of different grades of breast cancer.

Keywords: Velocity/Flow, Brain, Cerebrovascular health & arterial stiffness

Motivation: Arterial stiffness, a key marker of cardiovascular health, can be monitored by measuring pulse-wave velocity. However, current methods do not extend into the brain due to an inability to resolve the pulse waveform on a beat-to-beat basis for intracranial arteries.

Goal(s): Our goal is to demonstrate the concept of measuring intracranial pulse-wave velocity using dynamic inflow magnitude contrast (DIMAC) MRI.

Approach: We measured intracranial pulse-wave velocity by measuring the pulse-wave delay between internal carotid and middle cerebral arteries.

Results: The arterial pulse-wave delay between the internal carotid artery and the middle cerebral artery was 29±14ms, corresponding to a pulse-wave velocity of 6.8±2.2m/s.

Impact: These results are the first stage in establishing pulse-wave velocity as a new non-invasive MRI biomarker for cerebrovascular health, providing a novel tool to investigate the role of arterial stiffness in healthy ageing and brain pathology.

Keywords: Perfusion, Velocity & Flow, Cerebrovascular reactivity

Motivation: Caffeine, one of the widely used psychoactive substances is known to reduce basal cerebral blood flow (CBF). However, its effect on vasodilatory capacity has not been characterized

Goal(s): To evaluate the impact of Caffein on cerebrovascular reactivity (CVR)

Approach: 8 healthy caffeine-naïve volunteers were scanned for baseline (pre-caffeine) and post-Caffeine CVR measurements using BOLD MRI and phase-contrast MRI (PC)-MRI during normal air breathing and hypercapnia using 5% CO2 enriched gas mixture

Results: There was a significant reduction in blood-flux (BF) during room-air (p=0.002) and hypercapnia (p=0.0015) post caffeine administration (variation=33.7% and 41.3% respectively). PC-CVR and BOLD-CVR were reduced by 32.7%(p=0.006) and 22.5%(p=0.006) respectively

Impact: This study's findings provide valuable insights into the impact of caffeine on cerebrovascular reactivity (CVR), revealing a significant reduction after caffeine intake. Findings would be beneficial in reducing the inter-subject variability of CVR by improving the sensitivity in detecting abnormalities.

Keywords: New Devices, Motion Correction, Biofeedback

Motivation: Biofeedback during radiotherapy simulation and treatment on conventional linacs improves the accuracy and reproducibility of patient alignment, especially those using breath-hold. However, commercial devices cannot operate in MR environments.

Goal(s): The goal of this study was to develop an MR-compatible biofeedback device and test its utility in volunteer studies.

Approach: The device consists of a single-board computer and addressable LED strip, which was automatically controlled by tracking anatomic motion in CINE MR images.

Results: Initial volunteer studies using the device in an MR linac demonstrated that biofeedback-guided breath-hold resulted in more accurate and consistent target alignment compared to self-guided breath-hold.

Impact: MR-compatible biofeedback devices can facilitate and improve breath-hold techniques for MR-guided radiotherapy simulation and treatment. Furthermore, incorporation of CINE MR images to measure changes in internal anatomy improves target alignment relative to existing devices that use surface imaging.

Keywords: Phantoms, Phantoms, ASL, perfusion

Motivation: During longitudinal studies it is important to ensure quality assurance of MRI data. Here we use a commercially available perfusion phantom for QA of ASL in a 40-patient placebo-control study.

Goal(s): The goal of this study was to assess the utility of a perfusion phantom for QA of ASL MRI in a clinical trial setting.

Approach: Inversion recovery T1 measurements and pseudo-continuous ASL at 3 different phantom flow rates were scanned periodically on a 3T MRI system over a 36 month period.

Results: Once data due to errant phantom behaviour was discounted, the QA metric of labelling efficiency was in a reasonable range.

Impact: By acquiring independent T1 measures of the phantom’s perfusate, then computing ASL perfusion maps and comparing against the phantom’s pump flow rate, a useful QA metric can be established that can provide confidence in acquired subject ASL data.

Keywords: Safety, Safety

Motivation: MR safety guidance formulated by experts from the MR community build upon a combination of experience and technical expertise. MR safety guidance may benefit from assessment based on general workplace safety practices.

Goal(s): To critically assess MR safety guidance and practices using the hierarchy of hazard controls.

Approach: MR safety practices were scored using a 5-point scale derived from the hierarchy of hazard controls, in several areas of risk for MR personnel, patients, and non-MR personnel.

Results: Hazard controls for non-MR personnel had consistently high effectiveness scores, while those for for MR personnel and patients had moderate effectiveness scores, with a greater range.

Impact: The analysis presented in this work could serve as a tool to analyse choices made in the deployment of safety measures, to motivate decision- or policy-making, or as a tool for assessment of MR safety programs.

Keywords: High-Field MRI, High-Field MRI, 7T, 3DTOF, Non-contrast MRA, High gradient performance, microvascular

Motivation: Non-contrast 3DTOF demonstrates visualizing the cerebral microvasculature at 7T MRI, but the scan time is too long, typically about 10 minutes due to requiring high spatial resolution.

Goal(s): Our goal was to reduce the acquisition time by a factor of 2.

Approach: We utilize FLEXA on the high gradient performance system at 7T.

Results: Comparable image quality to 3DTOF was obtained with FLEXA technique with a factor of 2 decrease in acquisition time.

Impact: The improvement in acquisition speed through FLEXA comes from the strategy of short TR with thin slab and opens new standard for scan protocol optimization at 7T MRA.

Keywords: Phantoms, Whole Body, Whole-body diffusion-weighted imaging

Motivation: Poor signal-to-noise ratio (SNR) of whole-body diffusion-weighted images (WB-DWI) impacts the diagnostic exam quality in whole-body MRI. Evaluating SNR of WB-DWI using healthy volunteers is challenging when developing imaging protocols for multi-centre studies. 

Goal(s): Develop a phantom for assessing whether a proposed WB-DWI protocol will provide adequate SNR in patient examinations.

Approach: A phantom was developed which replicated relevant MR properties of WB-MRI patients. We measured SNR using the phantom and qualitatively graded SNR in subjects. 

Results: Good correlation was found between the phantom and the subject data and a discrimination threshold between good and poor quality exams was determined.

Impact: A phantom can be used to assess the SNR of WB-DWI protocols and shows good correlation with qualitative image quality, enabling faster, quantitative optimisation of SNR in WB-DWI protocols when setting up multi-centre studies.

Keywords: PET/MR, PET/MR, MR Priors, Motion Correction

Motivation: Using MR-priors in PET reconstruction has been performed in numerous studies. While MR-priors provide more SNR and better resolution ti PET-images, without considering motion there may be misalignment between PET and MR images which leads to crosstalk artifacts. Another concern is mismatch between MR and PET images which can potentially affect the final PET image.

Goal(s): To compare the two most-widely used PET recon with MR-priors methods: the Bowsher's method and MRgBSREM.

Approach: We created an MR-series with severe line artifacts and used in as MR-priors in both methods.

Results: We have shown that MRgBSREM handles these mismatches better than the Bowsher’s method.

Impact: MRgBSREM is more robust to mismatches between PET and MR. This is achieved by adding a PET-seed image to identify similar-voxels which avoids the situation in which voxels with significantly different PET-uptake would be considered similar based only on MR-images.

Keywords: Neurofluids, Brain, Volumetric strain; Brain tissue deformation

Motivation: The heartbeat causes deformations and volumetric strain in the surrounding brain tissue which can be observed using Displacement Encoding with Stimulated Echoes (DENSE) MRI. Local variations in volumetric strain reveal both expansion and compression in individual voxels or regions.

Goal(s): To offer insights into the heterogeneous patterns seen in volumetric strain maps.

Approach: Volumetric strain data of nine subjects was included and clustered (k-means) into four clusters.

Results: Both expanding and compressing clusters were found in all subjects. Compressing clusters were mostly found at the periphery of the brain and possibly reflect structures facilitating fluid movement (sulci and veins). 

Impact: Clustering volumetric strain data into individual clusters with similar voxels reveals distinct volumetric strain profiles in DENSE data. Potentially, these clusters can be used to cleanup DENSE data from fluid-related artifacts to make the data specifically reflect brain tissue strains.     

Keywords: In Silico, Artifacts

Motivation: RF shielding up to 95% in stents creates image artifacts that can impact an accurate diagnosis of vessel patency after stent implantation. 

Goal(s): The association between design parameters of novel venous stents and their RF shielding properties were investigated.

Approach: Therefore, field simulations and measurements were performed of the relative induced B₁ in venous stents with different lengths and cell geometries. 

Results: Edge length of the stent cells and their orientation relative to the B₁ transmit field determine the amount of RF shielding.

Impact: The work provides information about the correlation of venous stent geometries and RF shielding artifacts. Sequence parameters e.g. excitation flip angle could be adapted for different stent models to possibly achieve higher intraluminal signal in post-implantation MRI venography.

Keywords: Gradients, Gradients

Motivation: To assess diffusion metrics on a cellular scale necessitates much higher gradient amplitudes. A local nonlinear breast gradient coil needs methods of controlling the field variation during the coil design stage.

Goal(s): Control the y-axis gradient variation of a double layer local breast gradient coil to achieve far above 1T/m in the whole target region. 

Approach: Incorporation of a constraint to control the variation of the gradient along the y-axis with a double-layer current-carrying surface.

Results: A non-linear local human breast gradient coil with a gradient strength between 1.61 and 3.74 T/m for a current of 650 A was designed.

Impact: The coil design with a subsequent implementation paves the way to the application of novel diffusion imaging techniques for the detection and characterization of breast cancer.

Keywords: Phantoms, Precision & Accuracy, 3D Printing, Phantoms, Quantification, Material Characterisation

Motivation: 3D printed materials offer the capability of manufacturing custom phantoms or coil prototypes. While materials for other modalities (such as CT) are widely available, the same cannot be said for MRI.

Goal(s): The goal of this project was to characterise a wide range of 3D printed materials for use in MRI. 

Approach: Using standardised sample sizes, and relaxation property mapping sequences.

Results: Some materials, such as Nylon, proved invisible to MRI. Other materials, such as OrganLike, showed relaxation properties similar to those in the brain at 3T. 

Impact: 3D printing offers the potential to rapidly create low cost, reproducible prototypes for MRI. This work provides an extensive list of the properties of materials, aiding others in narrowing down a material of choice. 

Keywords: High-Field MRI, Bone, Diabetes bone, UTE-MRI, MMF, Exchange rate

Motivation: Increased risk of fractures in patients with type-2 diabetes mellitus (T2DM) despite higher average bone mineral density is unexplained with routine diagnostic tools like DEXA and CT. 

Goal(s): This study aimed to examine the feasibility of using ultrashort echo time (UTE) magnetization transfer (MT) modeling to detect the potential differences between T2DM and normal rats.

Approach: The macromolecular fraction (MMF) and proton exchange rate (k_ab) from UTE-MT modeling on the tibial bone of Zucker diabetic fatty (ZDF) and Zucker lean (ZL) rats were compared.

Results: There was a significant difference in MMF and k_ab measures between the two groups.

Impact: The MMF and kab measures can detect potential bone alternations related to T2DM which may help to better understand the pathogenesis of T2DM bone fractures. 

Keywords: Phantoms, Quantitative Imaging, Reproducibility

Motivation: Variability and bias of diffusion parameters need to be assessed to separate the measurement-induced variability from biological effects.

Goal(s): Our goal was to assess the bias and variability of mean diffusivity (MD) from the Diffusion Tensor Model using a traceable phantom.

Approach: The NIST/QIBA phantom was scanned multiple times using two different head coils on the same system. The differences between MD measured using different head coils and bias to NIST-reported values were investigated.

Results: A significant difference in measured MD with two coils can be found across multiple vials. Both coils introduce non-negligible bias to NIST-reported values for lower MD values.

Impact: Using traceable phantoms is essential in the development of Diffusion MRI-based Quantitative Imaging Biomarkers (QIBs). Different head coils can introduce significant differences across MD values and should be treated as confounding factors in QIBs studies.

Keywords: System Imperfections, System Imperfections: Measurement & Correction, Vendor-Neutral Automated Quality Assurance

Motivation: Ensuring comparable performance of MR protocols across vendors and over time duration.

Goal(s): To implement an easy-to-use vendor-independent quality control pulse sequences and data analysis routines.

Approach: Relying on Pulseq as a vendor-independent MR pulse sequence environment, we implemented the established quality assurance protocols (ACR/fBIRN). Following the image reconstruction from the acquired data, performed either on the scanner or off-line in Gadgetron, images are analyzed by the open-source Matlab pipeline.

Results: The proposed protocol has been tested on three 3T Siemens scanners with over a decade difference in the manufacturing date and has been successfully executed on a 3T GE scanner.

Impact: The proposed protocol and post-processing scripts allow for easy and streamlined quality assurance, contributing an essential component for Pulseq to become usable in large scale multicenter imaging studies.

Keywords: High-Field MRI, High-Field MRI

Motivation: Ultra-high field MRI of the brain suffers from an increased B₁⁺ inhomogeneity as well as involuntary motion artifacts when very high spatial resolution is targeted.

Goal(s): Integration of a FatNav technique into pTx Universal Pulse sequences would be beneficial to reach the best image quality at very high resolution.

Approach: An MP(2)RAGE sequence using GRAPE pTx universal pulses was modified to integrate a FatNav module. High-resolution protocols were acquired in vivo on the brain.

Results: Very high-quality images were obtained throughout the brain and cerebellum thanks to FatNav motion correction and pTx Universal Pulses.

Impact: The ultra-high field community targetting high-resolution protocols on the whole brain would benefit from a FatNav-enabled PASTEUR package to bring robust protocols against B₁⁺ inhomogeneities and involuntary motion of the head.

Keywords: Parallel Transmit & Multiband, Brain, bSSFP, parallel transmission, transmit field inhomogeneity

Motivation: Elimination of the transmit field inhomogeneity effects in the brain for T₂ contrast at 7T.

Goal(s): Removing the transmit field inhomogeneity effects in balanced steady state free precession acquisitions using Bilateral Orthogonality Generative Acquisitions method.

Approach: Bilateral Orthogonality Generative Acquisitions method is implemented  at dual channel parallel transmit 7T system using several balanced steady state free precession acquisitions with varying scan parameters to eliminate the transmit field inhomogeneity effects and reduce the banding artifacts effects via different combining schemes in the final image.

Results: T₂ contrast was obtained without the transmit field inhomogeneity effects but residual banding artifacts exists in the final images.

Impact: T₂ contrast can be achieved without the transmit field inhomogeneity effects in the brain using balanced steady state free precession sequence.

Keywords: Bioeffects & Magnetic Fields, Artifacts

Motivation: Inhomogeneity in measured multi-gradient echo (mGRE) field data corrupts reconstructions of quantitative susceptibility maps by obscuring the tissue field of interest with strong background field.

Goal(s): To extend the voxel spread function (VSF) library implementation to a nonzero phase offset and demonstrate improvements on QSM.

Approach: The measured field was estimated and inhomogeneity contributions computed using the extended library implementation. The inhomogeneity field was then estimated and subtracted from the total field to reduce the influence of strong background fields in the QSM reconstruction

Results: Inhomogeneity-informed field-fitting for QSM is shown to improve total field reconstructions of the brain, carotid, and cervical spine.

Impact: The voxel spread function (VSF) library implementation is extended to include initial phase offset contributions and reduce the effect of field inhomogeneity in quantitative susceptibility map (QSM) reconstruction.

Keywords: RF Arrays & Systems, RF Arrays & Systems, intraoral, dental, maxillofacial, RF coil

Motivation: In dental diagnostics MRI provides an improved soft tissue contrast for endodontic, craniomaxillofacial and implant applications with better visibility of anatomical structures.

Goal(s): An intraoral coil array is introduced for dental MRI to investigate feasibility, achievable signal gain and parallel acquisition performance.

Approach: Coil element size, material, and shape properties are investigated with respect to image SNR. The array is adjusted to the dental anatomy on a 3D phantom to estimate the sensitivity and the g factor of parallel imaging.

Results: Intraoral coil array improves SNR, spatial resolution and provides a homogenous B₁ receive profile. It allows parallel imaging to minimize measurement time.

Impact: Intraoral coil arrays can enhance MRI of small dental structures, required for endodontics, while increasing the spatial coverage in oral cavity. Compared to single-loop coils, a more homogeneous receive sensitivity can be achieved and parallel imaging becomes feasible.

Keywords: Neurofluids, Alzheimer's Disease, Aging, Brain, Blood Vessels, Cardiovascular, Dementia, Flow, Neuro, Neurodegeneration, Neurofluids, Velocity & Flow

Motivation: Cardiac driven motions may affect Alzheimer's Disease.

Goal(s): The goal was to provide preliminary results on the success of measuring the displacement and strain of brain tissues due to Cardiac Arterial Pulsations (CAPs) using DENSE (Displacement ENcoding with Stimulated Echoes) in patients with AD and age-comparable controls.

Approach: DENSE scan data from a sample of 133 volunteers was processed and evaluated for trends in the derived displacement and strain information.

Results: High variability was found in the displacement measures, and future work is needed to determine the confounding factors behind the variability and to what degree those factors can be minimized going forward

Impact: DENSE MR was used to measure displacement of brain tissue from cardiac effects in Alzheimer’s disease (AD) patients and age-comparable controls. Preliminary results are inconclusive and future work is needed to determine what confounding factors are affecting the displacement measures.

Keywords: RF Arrays & Systems, RF Arrays & Systems

Motivation: Hyperpolarized ¹³C MR imaging is of particular interest in cancer applications such as tumor staging and monitoring treatment response as it provides unique real-time metabolic information.

Goal(s): This work is to develop a novel double-tuned RF coil that can achieve uniform excitation and highly sensitive acquisition for ¹H/¹³C MR imaging at 7T.

Approach: The numerical electromagnetic simulation was utilized to evaluate the feasibility and performance in s-parameters and B₁⁺ field distributions. The bench tests were conducted to further validate the performance.

Results: These simulated and measured results indicated that the development of an efficient ¹H/¹³C RF coil for MR imaging at 7T.

Impact: A novel double-tuned RF coil system that can achieve uniform excitation and highly sensitive acquisition for ¹H/¹³C MR imaging at 7T was developed. This coil system has the potential to be used for hyperpolarized ¹³C MRI at ultra-high field.

Keywords: Small Animals, Brain, TMS, CBV, basal metabolism

Motivation: Transcranial magnetic stimulation (TMS) shows promise as a therapeutic intervention for many neuropsychiatric conditions. Yet, pinpointing the appropriate stimulation target remains elusive.

Goal(s): To identify brain regions affected by prolonged cocaine exposure, and to identify potential targets for focal TMS.

Approach:  Basal CBV were mapped by injecting iron-oxide contrast. Multi-echo gradient echo and spin echo sequences were employed to generate maps of 05732972-a323-4027-8676-ccee7744913e">R2. 

Results: Many brain regions, including the prelimbic cortex, exhibited a significant decrease in basal CBV following prolonged cocaine exposure. The prelimbic cortex is situated relatively close to the cortical surface and presents a promising candidate for TMS.

Impact: Likely the first attempt to identify TMS targets using the CBV mapping approach.

Keywords: Biology, Models, Methods, Multiple Sclerosis, blood brain barrier permeability

Motivation: Blood brain barrier (BBB) dysregulation is one of the earliest signs of multiple sclerosis (MS) and the mechanism underlying BBB breakdown is not completely understood. 

Goal(s): We sought to use the non-obese diabetic experimental allergic encephalomyelitis (NOD-EAE) mouse model of secondary progressive MS to understand BBB breakdown in efforts to explore potential MS therapeutics.

Approach: MRI was used to quantify BBB permeability metrics using gadolinium contrast agent. 

Results: We quantified the spatial and temporal characterization of BBB permeability in NOD-EAE mice with progressing disease using MRI. These quantifying parameters can potentially be used to test the effect of therapeutic agents on BBB breakdown.

Impact: The NOD-EAE mouse model of secondary progressive multiple sclerosis (SPMS) can potentially be used to assess blood brain barrier characteristics using contrast-enhanced MRI in efforts to test therapeutic agents that can be used in the treatment of SPMS. 

Keywords: Neurofluids, Neurofluids

Motivation: This research aims to explore the relationship between CSF dynamics and BOLD signals using EPI-based resting-state fMRI data from an open database of rat models.

Goal(s):  Rodents and humans are compared regarding CSF dynamics and the BOLD signal.
 

Approach: The study computes temporal correlations between CSF pulse, CSF edge, and the BOLD signal.
 

Results:  While in resting state, both CSFpulse and CSFedge indices were correlated with global BOLD, exhibiting a stronger correlation for CSFedge and varying peak correlations at -1s, -10s, and +11s. The temporal correlation between CSF dynamics and global BOLD indicates differences in CSF physiology in humans compared to smaller animals.

Impact: The study results imply differing correlation coefficients between CSF dynamics and the BOLD signal, suggesting promising pathways for deeper investigations into brain CSF dynamics in small animal models and neuroscientific advancements using EPI-based fMRI database.   

Keywords: Preclinical Image Analysis, Preclinical, Amyotrophic lateral sclerosis (ALS)

Motivation: Amyotrophic lateral sclerosis is still an understudied disease, and early detection is important.

Goal(s): This study aimed to clarify the transition of the pathophysiology and evaluate the relationship between the abnormalities detected on magnetic resonance imaging and the development of clinical symptoms by combining the T₂ map data obtained in the previous study with behavioral analysis.

Approach: Behavioral analysis was performed on four parameters, namely, bodyweight, general condition, hind-foot reflex test, and landing foot-splay test. Correlation analysis of the behavioral analysis data and T₂ map data was performed in each voxel. 

Results: A correlation was detected in the trigeminal motor nucleus.

Impact: Changes in the trigeminal motor nucleus are indicated to be closely related to early changes in amyotrophic lateral sclerosis (ALS). This finding is suggested to be useful for the early diagnosis of ALS in humans and research on novel treatments.

Keywords: Small Animals, Relaxometry

Motivation: Measuring T2* in mouse brains may therefore be of interest in assessing pathological models or innovative treatments directed against these pathologies.

Goal(s): The goal of this study was to obtain a high resolution and accurate T2* map of the mouse brain ex vivo and in vivo.

Approach: A 3D Multi Gradient Echo sequence with a drift compensation module was used and combined with an optimized fitting model which takes account the non-central chi distribution of the multi-channel gaussian noise.

Results: T2* map were obtained ex vivo and in vivo at 117 µm isotropic resolution in 20 minutes.

Impact: A high-resolution 3D Multi-Gradient Echo sequence sequence has been coupled with an optimized fitting algorithm to generate high resolution T2* map of the mouse brain. This method can be used to study preclinical stroke model.

Keywords: Biomarkers, Traumatic brain injury

Motivation: Imaging biomarkers accurately diagnosing and monitoring treatment response for chronic mild Traumatic Brain Injury (mTBI) are needed. 

Goal(s): This study aimed to experimentally validate and determine the performance of MRI biomarkers for monitoring chronic repetitive mild traumatic brain injury (mTBI). 

Approach: We used diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) to assess white matter injury in mouse brains with chronic mTBI and compared results with those for age-matched control brains. 

Results: Reduction in fractional anisotropy and increase of axial kurtosis were observed in the medulla of mTBI brains compared to the parameters in the same region of control brains.

Impact: It is new to monitor DTI and DKI parameters as noninvasive biomarkers in the brain with chronic mTBI.

Keywords: Preclinical Image Analysis, Brain Connectivity, axial diffusivity, marmosets, brain volume

Motivation: A direct examining the volume and diffusion indices of in vivo and ex vivo brains from the same individual is of great interest in linking clinical and histological studies.

Goal(s): The goal of this study is to investigate the relationship between brain volume and diffusion indices in brain using in vivo and ex vivo brains from the same individual.

Approach: Dwi indices and brain volumes of the whole brain, white matter, cerebral cortex, cerebellum, and 52 brain regions were compared in vivo and ex vivo from same individuals.

Results: For each DWI index, AD was correlated with volume change in 40 cortical regions.

Impact: This study successfully compared the same brain in vivo and ex vivo directly, and it sufficiently demonstrates the relationship between volume and diffusion indices. In particular, AD is most sensitive to regional volume changes.

Keywords: Small Animals, CEST & MT, Drugs, metabolites, antiretroviral, HIV, ART, gluatamate, creatine

Motivation: The neurotoxicity of antiretroviral (ARV) drugs has been reported to affect neurocognition.

Goal(s): In this study, we tried to link CEST results of ARVs and metabolites to elucidate the effects of ARVs on metabolic alterations.

Approach: Mice administrated with TDF/3TC/DTG were scanned using CEST MRI to detect 3TC (at 1 and 2 ppm) and TDF (at 3 ppm). Metabolites were also measured using CEST MRI.

Results: Results showed that when used in combination, MRI is not sensitive to the CEST contrasts of 3TC and TDF. Glutamate, creatine and NOE were affected by TLD indicating the toxicity of TLD.

Impact: The study showed the potential to use CEST MRI to elucidate the effects of antiretroviral drugs on the neuropathologic outcomes by testing the association of MRI measurements of ARVs and metabolic imaging results.

Keywords: Preclinical Image Analysis, Data Acquisition, Flow measurment

Motivation: Non-invasive measurement of intracranial flow can elucidate fluid interactions within the brain. However, it's essential to determine the range of velocities that can be reliably measured using this approach.

Goal(s): Our aim is to assess the ability of PC MRI "Flow Compensated Fast low angle Shot (fcFLASH)" based sequence for velocity measurements in small blood vessels and cerebrospinal fluid in rats.

Approach: A phantom is built to simulate fluid flow. Measurements are repeated for each velocity range.

Results: Non gated fcFLASH-based PC MRI allows a rough estimation of flow velocities on the order of 0.5 cm/s.

Impact: our study shows that the fcFLASH-based PC MRI can be used for a rough estimation of a small fluid circulation. A methodological development is necessary for a reliable measurement.

Keywords: Flow, Cardiovascular

Motivation: Mitral valve regurgitation (MVR) is the most common heart valve diseases, but accurate quantification of MVR has been limited due to the dynamic motion of valves. 

Goal(s): We hypothesize that volumetric flow information by 4D flow MRI with the compensation of MV motion could address this challenge.

Approach: MVR was measured using a simulated-in vitro model by comparing with actual flow from pump, ultrasound, and 4D flow MRI with/without a mitral registration algorithm.

Results: PISA method overestimated MVR. 4D flow MRI without algorithm seems to have similar the amount of actual MVR, while it decreased with employing algorithm.
 

Impact: Accurate quantification of regurgitation plays an important role for diagnosing patients with valvular diseases. This study would have a chance to measure accurate MVR quantification and quantify complex intracardiac blood flow using 4D flow MRI.

Keywords: Heart Failure, Cardiovascular

Motivation: Heart failure is a clinical syndrome in which the heart is unable to pump blood efficiently, and Heart Failure with Reduced Ejection Fraction (HFrEF) with the left ventricular ejection fraction (LVEF) < 40% serve as high morbidity and mortality.

Goal(s): The aim of this study is to investigate the hemodynamic environment in left ventricle of different phases.

Approach: 4D flow technology could offer non-invasive method for quantifying the alternatives of intracardiac blood flow components in HFrEF with and without MACE.

Results: Results showed reduced DF-KEiESV, DEF-KEiESV, higher RVo and peak E-wave RVo-KEiEDV were confirmed in MACE+ group when compared with MACE- group.

Impact: The hemodynamic analysis in HFrEF when compared patients with and without MACE illustrated significant alternative of intracardiac blood flow components by using 4D flow technology, which could offer valuable insight for further assessment and treatment planning. 

Keywords: Myocardium, Cardiomyopathy

Motivation: The risk stratification for implantable cardioverter-defibrillators (ICD) placement in high-risk patients with hypertrophic cardiomyopathy (HCM) has challenges.

Goal(s): The aim of this study is to investigate the connection between left atrial and left ventricular strain and sudden cardiac death (SCD) risk stratification by feature-tracking CMR.

Approach: All patients were satisfied by the 2020 AHA/ACC HCM Risk-SCD model with feature-tracking CMR for assessing left atrial and left ventricular strain.

Results: Results demonstrated that both left atrial and left ventricular strain show obvious attenuation in high-risk group than low-risk group. LVGRS+LARS combined model exhibited a superior diagnostic value for identifying high-risk SCD.
 

Impact: The LVGRS+LARS combined model could provide additional predictive value for improving SCD risk stratification in clinical practice, which may provide vital insights into the judicious clinical utilization of ICD to reduce SCD in HCM.