Motivation: Finding ways to use the PROPELLER sequence as an even more the robustness method to motion.

Goal(s): This study investigates whether repositioning the head after pausing during PROPELLER imaging, enhances image quality.

Approach: This study investigated whether image quality improved with repositioning the head after a pause during PROPELLER method with a brain phantom and driver system.

Results: We found that image quality improved at all rotational angles and that pausing multiple times was effective depending on the frequency of motion.

Impact: Incorporating a pause function into the PROPELLER method is expected to be clinically applicable as a practical means to further improve the robustness of the PROPELLER method to motion.

Motivation: In nonalcoholic fatty liver disease (NAFLD), hepatic fibrosis is strongly associated with patient survival. Diffusion MRI has been proposed to assess liver fibrosis, but this evaluation is hampered in hepatic steatosis. 

Goal(s): Our goal was to evaluate the diagnostic performance of non-Gaussian diffusion MRI in assessing liver fibrosis in 250 patients with NAFLD. 

Approach: We developed a method to calculate the non-Gaussian diffusion coefficient, based on non-linear regression and fat correction. 

Results: With this corrected diffusion method, NAFLD patients with liver fibrosis could be differentiated from patients without it.

Impact: With this corrected diffusion method, NAFLD patients with liver fibrosis could be differentiated from patients without it.

Keywords:

Motivation: Proton therapy is a relatively new option for treatment of uveal melanomas. For adequate treatment, accurate measurement of tumour dimensions and tumour-marker distance is essential. The dedicated MRI protocol for uveal melanoma patients developed in our institution could be helpful.

Goal(s): In this study, we compared MRI-based measurements with conventional ophthalmic measurements.

Approach: Retrospectively, MRI based measurements of  tumour dimensions and marker-tumour distances in 23  patients were compared with conventional ophthalmic measurements and evaluated.

Results: MRI allowed for the three-dimensional assessment of the tumour. In specific patients, it provided a more reliable measurement of tumour dimensions and marker-tumour distances.

Impact:  Dedicated ocular MRI proved to be  a reliable tool for measurements required in the planning of proton therapy in patients with uveal melanoma.

Keywords:

Motivation: Although anterior talofibular ligament (ATFL) repair is commonly performed as part of chronic lateral ankle instability (CLAI) surgical treatment, there are no reports that quantitatively evaluate the repaired ATFL.

Goal(s): Our goal was to investigate longitudinal changes in T2* values within ATFL after primary ATFL repair.

Approach: We compared preoperative and 6 months postoperative T2* values of the ATFL using high-resolution T2* map images.

Results: There was a 118% change in the T2* value at 6 months after ATFL repair, including a 130% change on the fibular side.

Impact: These findings provide basic data to help understand the healing process after ATFL repair surgery and provide quantitative values may help predict recurrence and clinical outcomes.

Motivation: Breast magnetic resonance imaging (MRI) is a sensitive technique for staging and screening for breast cancer, however, it is susceptible to motion artifacts and inhomogeneous fat saturation.  

Goal(s): Demonstrate that 3D radial mDIXON acquisition provides robust motion suppression and homogenous fat saturation.

Approach: Compared image quality of 3D radial mDIXON to 3D Cartesian mDIXON and 3D Cartesian with spectral fat saturation.  Advanced postprocessing was also performed on the 3D radial mDIXON to see if additional diagnostic information could be obtained.  

Results: 3D radial mDIXON of the breast showed constantly high image quality with less motion artifacts and homogenous fat suppression.    

Impact: 3D radial mDIXON provides many advantages over Cartesian spectrally fat saturated acquisitions.  The radial acquisition is less sensitive to motion artifacts.  mDIXON provides consistent fat suppression compared to spectral fat suppression.  Advanced postprocessing of mDIXON images provides further diagnostic utility.   

Motivation: The low sensitivity of liver ultrasound precludes the diagnosis of hepatocellular carcinoma, which led to an increased interest in abbreviated liver mri as an alternative diagnostic tool

Goal(s): The goal of this research study was to assess the diagnostic performance of abbreviated liver MRI for the diagnosis of hepatocellular carcinoma

Approach: This study is a literature review, in which the latest available literature relevant to the topic was assessed with the intent of answering the research question

Results: Liver AMRI shows higher sensitivity than ultrasound for diagnosis of HCC and its diagnostic performance is comparable to that seen in a conventional protocol

Impact: In summary, liver AMRI should be considered as an altertative strategy to ultrasound for HCC surveillance. Future research including prospective studies, cost-effectiveness assessments and a consensus on an ideal AMRI protocol should be carried out.

Keywords: Image acquisition: Fast imaging, Transferable skills: Safety

Keywords: YIA, White Matter

Motivation: The mechanisms underlying age-related structural neurodegeneration are not well understood, limiting our knowledge of atypical versus typical aging. 

Goal(s): Our goal was to use data involving advanced hemodynamic and structural MRI techniques tailored to white matter to characterize the aging process.

Approach: We used data from a large cohort of the Human Connectome Project in Aging to investigate the relationship between brain blood flow and white matter tract microstructural integrity.

Results: We found strong relationships between white matter hemodynamics and tract integrity that were affected by both age and sex.

Impact: These findings could reveal potential underlying physiological mechanisms behind structural changes during typical aging. This could help us understand healthy brain aging and encourage future research to target hemodynamic biomarkers to understand neurodegeneration.

Keywords: Flow, Blood vessels, 4D Flow MRI

Motivation: The motivation of this work is to understand the hemodynamics parameters in subjects with carotid web (CaW), atherosclerosis subjects with similar luminal narrowing, and normal subjects using 4D Flow MRI.

Goal(s): The main goal of this work is to quantify parameters related to vascular dysfunction including wall shear stress (WSS) and oscillatory shear index (OSI). 

Approach: 4D Flow MRI was utilized to prospectively scan subjects with CaW and hemodynamic parameters were compared to subjects with mild atherosclerosis and healthy volunteers.

Results: The results show that subjects with CaW have larger regions of complex blood flow represented by low WSS and high OSI. 

Impact: This study improves our understanding of disturbed hemodynamics caused by CaWs in comparison to atherosclerosis, which may explain the mechanism of thrombus formation and lay the groundwork for stroke risk assessment in patients with CaW.

Keywords: YIA, Neuro

Motivation: To address the unmet need for a cross-vendor, multiparametric technique to facilitate data pooling across sites.

Goal(s): To evaluate a vendor-standardized multiparametric mapping scheme based on 3D-QALAS for whole-brain T1, T2, and proton density (PD) mapping.

Approach: Intra-scanner repeatability and inter-vendor reproducibility were evaluated in vivo on five different 3T systems from four vendors (GE, Philips, Siemens, and Canon). Patients with multiple sclerosis were scanned on systems from different vendors to assess the feasibility of the scheme in real-world clinical settings.

Results: 3D-QALAS provided T1, T2, and PD with coefficient of variations <4.0% using 3T scanners from different manufacturers.

Impact: The four major vendors used in this study constitute a considerable portion of the global installation base, demonstrating the value of cross-vendor quantitative technique 3D-QALAS for imaging in clinical sites with multiple vendors, as well as in multicenter research settings.

Keywords: Non-Array RF Coils, Antennas & Waveguides, Non-Proton, Multinuclear, RF coil, X-nuclei

Motivation: There are 118 elements. Nearly all elements have NMR active isotopes and 39 different nuclei have been shown to have biological relevance. Despite this, most of today’s MRI is based on only one nucleus – ¹H.

Goal(s): To significantly reduce the cost and complexity of imaging all potential nuclei.

Approach: We present the Any-nucleus Distributed Active Programmable Transmit Coil (ADAPT Coil), with fast switches integrated into the coil itself which allows it to selectively excite any nucleus using digital controls.

Results: Using the ADAPT Coil, we acquired ¹H, ²³Na, ²H, and ¹³C phantom images and ¹H and ²³Na ex vivo images at 3T.

Impact: The ADAPT Coil enables arbitrary nucleus excitation in high field MRI, significantly reducing the cost and technological barriers of clinical translation of X-nuclei research. X-nuclei benefits include improved early diagnosis and treatment evaluation for cancer, osteoarthritis, Alzheimer’s, and many more.

Keywords: YIA, Diffusion/other diffusion imaging techniques, Diffusion-weighted MR spectroscopy, rodent brain, high field, SPECIAL, glutamine, J-coupled metabolites

Motivation: Diffusion-weighted MR spectroscopy (dMRS) uniquely probes cell-specific tissue microstructure in vivo but most sequences suffer from long TE leading to signal loss by J-evolution and T₂ relaxation.

Goal(s): To propose an alternative dMRS sequence (DW-SPECIAL) with a shorter TE while preserving the benefits of the current gold-standard rodent sequence at high field (STE-LASER).

Approach: DW-SPECIAL was tested in vivo in the rat brain and compared to STE-LASER.

Results: DW-SPECIAL halved the minimum TE while reducing specific absorption rate compared to STE-LASER, thereby 1) improving the J-coupled metabolites’ diffusion properties estimation and 2) offering a new candidate sequence for human dMRS.

Impact: With its shorter TE, our newly proposed DW-SPECIAL can serve as an alternative to STE-LASER when strongly J-coupled metabolites like glutamine are investigated, thereby extending the range of accessible metabolites in the context of diffusion-weighted MRS acquisitions at high field.

Keywords: YIA, Fetus

Motivation: Small for gestational age (SGA) fetuses are undernourished and at higher risk for adverse outcomes; however, conventional assessment methods exhibit limited sensitivity.

Goal(s): To stratify perinatal risk using MRI-based body composition metrics.

Approach: TruFISP and 2-points Dixon images were used to compute the total fetal volume (TFV), fat-to-body volume ratio (FBVR) and adipose tissue fat signal fraction (FSF) using deep-learning segmentation.

Results: SGA fetuses (N=40) with lower FBVR were more likely to require obstetric interventions because of non-reassuring status, while those with reduced TFV were prone to adverse neonatal outcomes. The model’s sensitivity/specificity rates are 85.7%/87.5% and 82.35%/86.4%, respectively.

Impact: Quantifying fetal body composition through MRI can offer additional insights into the severity of small for gestational age complicated pregnancies and may help in stratifying perinatal risk.

Keywords: YIA, Data Acquisition, Acquisition & Analysis, Body, Lung, Lung water, Heart failure, Translational Studies

Motivation: Quantification of lung water during exercise is of interest for early diagnosis of heart failure.

Goal(s): To develop a time-resolved 3D MRI method to quantify lung water in transitions between rest and exercise.

Approach: We derive quantitative time-resolved lung water density (LWD) maps using a motion corrected sliding-window image reconstruction. We included 12 healthy controls and 2 patients with heart failure, and a porcine model of mitral regurgitation (n=5).

Results: We measured a peak exercise ΔLWD=16±6.8% in controls, but detected no changes during rest (ΔLWD=-1.4±3.5%, p=0.18). Accumulation rates were slower in patients (2.0±0.1%/min) vs controls (2.6±0.9%/min). Animals developed ΔLWD=3.3±1.5%.

Impact: Exercise-induced changes in lung water can be dynamically quantified using a continuous 3D MRI acquisition with a sliding-window and motion corrected image reconstruction, which may have clinical utility in unmasking latent heart failure at early stages of disease.

Keywords: YIA, Hybrid & Novel Systems Technology, Physics & Engineering, Acquisition & Reconstruction, New Trajectories & Spatial Encoding Methods, Acquisition Methods, nonlinear gradient

Motivation: The inherently slow MRI scans can be accelerated through rapid modulation of nonlinear gradient fields; however, its fundamental mechanisms and limits remain incompletely understood and validated.

Goal(s): We investigate accelerated MRI with flexible modulations of nonlinear B₀ fields using a custom-built local B₀ array.

Approach: The sampling theory is extended to rigorously compare nonlinear field modulation schemes in a quantitative k-space. A novel field calibration technique is proposed to enhance reconstruction. With safety evaluations, we perform in-vivo accelerated scans.

Results: Our in-vivo 2D FLASH scans make significant steps to speed up MRI with local B₀ array, achieving eight-fold joint acceleration with parallel imaging.

Impact: For the first time, the sampling efficiency of nonlinear gradients in the entire k-space is quantitatively visualized, allowing rigorous comparison of distinct B₀ modulations. Furthermore, the field estimation technique enables fast and robust in-vivo scans accelerated by flexible nonlinear fields.

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

Motivation: Cardiac cine MRI reconstruction is a natural high-dimensional problem that poses great challenges to deep learning.

Goal(s): To develop a new deep learning method that can work efficiently in cardiac cine MRI, even with limited training data.

Approach: In this work, the proposed method DeepSSL significantly alleviates training and generalization challenges of deep learning in cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling.

Results: Extensive results show that DeepSSL can work efficiently even with highly limited training data (5~10 cases), and provides state-of-the-art reconstructions while reduces data demand by up to 75%. It further shows robustness in prospective real-time MRI.

Impact: The proposed deep separable spatiotemporal learning (DeepSSL) significantly alleviates the training and generalization challenges of deep learning in high-dimensional cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling.

Keywords: AI/ML Image Reconstruction, Cardiovascular

Motivation: Cardiac CINE provides dynamic images of the heart for morphology and function assessment. Single-heartbeat CINE enables faster acquisition times and the study of heart rate variations, but conventional reconstruction methods incur significant computational cost.

Goal(s): This study aims to speed up single-heartbeat CINE reconstruction by using deep learning reconstruction.

Approach: We propose a novel, rapid, end-to-end deep learning pipeline for motion estimation and motion-corrected single-heartbeat CINE reconstruction with golden-angle radial acquisition.

Results: The network reconstructs each CINE slice in ~40 seconds (400 times faster than state-of-the-art), with comparable image quality, achieving SSIM values ranging from 0.75 to 0.84 across cardiac phases and slices.

Impact: The proposed approach enables reconstruction of single-heartbeat golden-angle radial CINE acquisition in ~40 seconds, making it clinically feasible. Single-heartbeat CINE could reduce scan times, achieve acquisitions of multiple slices in a single breath-hold and be robust to heart rate variations.

Keywords: AI/ML Image Reconstruction, Cancer, 4D-MRI\Enhancement\Temporal-compensation

Motivation: Four-dimensional Magnetic Resonance Imaging (4D-MRI) shows promise for motion management in abdominal radiotherapy. However, the prevalent undersampling often hampers its image quality. 

Goal(s): To enhance the image quality of 4D-MRI, we propose Tco-SEN, a deep-learning model to exploit its properties. 

Approach: Tco-SEN employs a two-stage architecture and a customized loss penalty, enabling effective restoration of detailed features and preservation of anatomical structures.

Results:  Compared to state-of-the-art algorithms, Tco-SEN significantly enhances image quality by improving spatial resolution, reducing motion artifacts and noise, and preserving delicate structures. Furthermore, our method enhances the accuracy of subsequent motion modeling in 4D-MRI, highlighting its potential for clinical applications.

Impact: Tco-SEN effectively improves the image quality of 4D-MRI, benefiting more accurate tumor delineation and motion estimation. This advancement promotes the application of 4D-MRI in cancer radiotherapy, ultimately enhancing the accuracy of abdominal cancer radiation treatment.

Keywords: AI/ML Image Reconstruction, Cardiovascular, Self-Supervised learning, Feature learning

Motivation: Most existing deep learning-based MR image reconstruction methods are supervised learning, relying on fully-sampled images, which is challenging to acquire in practice.

Goal(s): We aim to leverage undersampled data in a self-supervised reconstruction framework to enhance expressibility and model performance.

Approach: We use information maximization methods to learn sampling-invariant features from undersampled images and incorporate them in a self-supervised reconstruction network.

Results: The proposed method can learn sampling-invariant features from undersampled data, which enhance the reconstruction performance, enabling self-supervised MR image reconstruction for up to 16× undersampling.

Impact: The proposed self-supervised feature learning strategy can extract sampling-invariant features from undersampled images, effectively assisting the reconstruction of undersampled cardiac cine MR imaging without requiring fully-sampled images. This feature learning strategy may also be advantageous for other downstream tasks.

Keywords: AI/ML Image Reconstruction, New Trajectories & Spatial Encoding Methods

Motivation: Sampling patterns in deep learning (DL) or compressed sensing (CS) based accelerated dynamic MRI reconstructions are typically chosen heuristically. k-t sampling patterns can be optimized to capture the spatio-temporal characteristics of dynamic MRI data more efficiently.

Goal(s): Our objective is to develop a method for optimizing k-t sampling patterns for dynamic MRI.

Approach: We extend the recently developed AutoSamp framework to dynamic MRI setting to jointly optimize k-t sampling and reconstruction. We test our method on a cardiac cine dataset.

Results: DL reconstruction with optimized k-t patterns using the proposed method produces higher quality results with reduced spatial and temporal artifacts.

Impact: Dynamic MRI reconstructions with learned sampling patterns improves reconstruction quality. The learned patterns can also provide insights about designing general k-t MRI sampling patterns.

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

Motivation:
Neural implicit k-space representations (NIK) enable binning-free respiratory-resolved MR reconstructions in a data-driven manner. The multi-dimensionality of MR, i.e., provided in dual-echo acquisitions, is expected to improve reconstruction performance and allows for further echo-processing.

Goal(s): A Dual-Echo-NIK that takes advantage of the redundant data present in two echoes and enables subsequent water-fat-separation.

Approach: We propose three Dual-Echo-NIK variants trained (1) individually, (2) jointly and (3) in an echo-modulated way. Motion-resolved echo and water-fat reconstructions are evaluated on a free-breathing phantom simulation and in-vivo.

Results: Quantitative simulations demonstrate improved performance for the modulated Dual-Echo-NIK. In-vivo reconstructions reveal sharper reconstructions when both echoes are utilized.

Impact: The Dual-Echo Neural Implicit k-space Representations indicate how echo information can lead to improved motion-resolved reconstructions, including subsequent water-fat separations. Echo-modulation can further enhance reconstruction performance and offers the potential to reduce acquisition times for training data.

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: AI/ML Image Reconstruction, Image Reconstruction

Motivation: State-of-the-art motion-resolved 4D MRI techniques lack sufficient spatial resolution and efficient acquisition and reconstruction for application in clinical practice.

Goal(s): To develop HD-Movienet, a deep learning-based method to efficiently acquire and reconstruct 4D MRI with approximately 1mm isotropic resolution using 3D radial acquisitions.

Approach: HD-Movienet uses accelerated half-spoke (UTE) and full-spoke (T1-weighted) 3D radial kooshball acquisition and image-time-coil deep learning 4D reconstruction without k-space data consistency.

Results: HD-Movienet can enable 4D MRI with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds to image patients with lung tumors.

Impact: Deep learning-based HD-Movienet reconstruction enables motion-resolved 4D MRI technique with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds for robust radiation-free imaging of patients with mobile tumors.

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction

Motivation: Interactive real-time MRI requires low reconstruction latencies. Deep learning-based methods are promising, but unrolled networks like the Variational Network have longer inference times than purely image-based methods.

Goal(s): Design and train a Variational Network with high reconstruction quality and inference times suitable for interactive real-time applications.

Approach: Modify the Variational Network architecture such that few unrolling steps are sufficient for high reconstruction quality with short inference times.

Results: The proposed architectural modifications allowed to halve the number of unrolling steps without compromising image quality, therefore enabling considerably shortened reconstruction times.

Impact: Two modifications to an unrolled Variational Network architecture for MRI reconstruction are proposed. These enable reconstructing interactive real-time cardiac cine MRI with high reconstruction quality while maintaining minimal reconstruction latency.

Keywords: Tumors (Pre-Treatment), Tumor, Glioblastoma

Motivation: Pre-operative glioblastoma (GBM) infiltration prediction models rely on manual infiltration risk (IR) prior segmentation which is tedious, requires expert input, and is highly variable. 

Goal(s): Automation is needed for fast segmentation. A data-driven method would account for GBM heterogeneity and be independent of specific MRI input for applicability to clinical protocols.

Approach: IR priors are grown using modified triplet loss with inter-prior and intra-prior terms to ensure priors are distinct from each other and maintain similarity within individual priors.

Results: TripleSeq generated more consistent IR priors compared to manual segmentation. TripleSeq-trained models showed good classification (> 85% mean accuracy) of ground truth infiltration.

Impact: Glioblastoma (GBM) infiltration inevitably leads to tumor recurrence and progression. We introduce an automatic method to generate infiltration risk priors for improved GBM infiltration machine learning prediction, which applied pre-operatively can identify at-risk peritumoral regions for targeted neurosurgery and radiotherapy.

Keywords: Aging, Aging, aging, structural imaging, radiomics, neuro

Motivation: A machine learning model capable of accurately estimating brain age could have a large clinical impact.

Goal(s): To apply radiomics analysis to morphological MR images and train a machine learning model capable of accurately estimating subjects’ age from radiomics features.

Approach: T1- and T2-weighted brain images of 725 healthy adults were used to extract 18324 radiomics features from bilateral caudate, putamen, and hippocampus, and used to train a stacking regressor machine learning model.

Results: Our machine learning model accurately estimated the subjects’ age with a mean absolute error of 4.77±0.35 years using radiomics features from T1-(45%) and T2-weighted(55%).

Impact: Investigating advanced machine learning methods to accurately estimate brain aging based on commonly used clinical MR images provides vital insights to further improve our understanding of brain changes in both healthy aging and neurodegeneration.

Keywords: Tumors (Pre-Treatment), Tumor, glioma, neuro-oncology

Motivation: Glioblastoma (GBM), a highly lethal brain tumor, poses a significant threat to patient survival, even after gross total resection (GTR).

Goal(s): This study explored whether radio-pathomic features from autopsy-trained models could predict survival in GTR-treated GBM patients.

Approach: The relationship between cell density and tumor probability (TPM) beyond the FLAIR hyperintense (FH) region, as well as a habitat-based labeling within FH was investigated. Cox regressions evaluated the impact of habitat volume and radio-pathomic characteristics within FH on survival.

Results: The study revealed that radio-pathomic features of FH predicted overall survival, suggesting the ability to identify infiltrative tumor ultimately missed by surgery.

Impact: In GTR-treated GBM patients, the presence of infiltrative tumor cells within and beyond FLAIR hyperintensity may predict patient prognosis and could be used for optimizing treatment.

Keywords: Tumors (Post-Treatment), Neuro

Motivation: The patients with glioblastoma (Gb) with poor prognosis and quality of life.

Goal(s): To construct a comprehensive model for predicting the prognosis of patients with Gb using a radiomics method and integrating tumor microenvironment .

Approach: In total, 149 patients with isocitrate dehydrogenase wild-type Gb were enrolled retrospectively. Selected the best feature combination related to Gb overall survival. Clinical-radiomics-TME models were established. 

Results: Lasso-Cox analyses were used to screen the factors related to OS in patients with Gb, including age, peritumoral edema, tumor purity, tumor-associated macrophages, and the 21 radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C-indices was 0.727. 

Impact: Considering the poor prognosis of IDH wild-type Gb, we explored additional prognostic risk factors and established a survival prediction model. The clinical-radiomics-TME comprehensive model showed a significant improvement compared to other models and was most effective in predicting patient survival.

Keywords: Tumors (Pre-Treatment), Tumor, Cerebrovascular reactivity; venous cerebral blood volume; bolus arrival time; hypercapnia; hyperoxia

Motivation: Current clinical practice assesses baseline vascular features and cerebrovascular reactivity with multiple techniques that involve the use of injected contrast and radioactive tracers. Obtaining this information requires numerous tests and visits, which increases patient stress and healthcare costs.

Goal(s): Our goal was to determine whether a multiparametric scan could conveniently and economically assess glioma hemodynamics with no exogenous contrast.

Approach: The technique involves sequential manipulation of CO₂ and O₂ in inspired gas while collecting BOLD MRI images to obtain CVR, vCBV, and BAT maps.

Results: Multiparametric maps correctly differentiated tumor and normal tissue with characteristics that may inform tumor classification.

Impact: We showed that an efficient multiparametric scan can map different vascular properties. These maps allow for tumor and healthy tissue differentiation and show qualitative traits that potentially informs tumor characteristics which could aid in the diagnostic evaluation of glioma patients.

Keywords: Tumors (Pre-Treatment), Diffusion/other diffusion imaging techniques

Motivation: The edema around a malignant glioma contains infiltrating tumor cells. The motivation for this study was to determine the characteristics of the edema of malignant glioma.

Goal(s): The goal is to evaluate the characteristics of edema around malignant gliomas using a combination of the oscillating gradient spin echo and pulsed gradient spin echo.

Approach: The ternary plot method is used to evaluate the characteristics of edema by using a plot of existing tissue as an internal reference.

Results: Edema of malignant gliomas showed a different distribution in relation to the internal reference in the ternary plot method compared to edema of other tumors.

Impact: Ternary plot method was used to present pixel values obtained from oscillating gradient spin echo and pulsed gradient spin echo, and existing tissues were evaluated as internal references, which was thought to enable evaluation of the histological properties of edema.

Keywords: Tumors (Pre-Treatment), Cancer, Microstructure, Diffusion, Spectroscopy

Motivation: Understanding the complexity of the tumour microenvironment is critical for understanding glioma progression and developing effective therapies. 

Goal(s): To develop a non-invasive imaging pipeline for characterization of the glioma tumor microenvironment.

Approach: Combining DW-MRI and DW-MRS to enhance the characterization of a spectrum of gliomas as a feasibility study.

Results: Changes in neuronal (tNAA) and glial (tCho) metabolites apparent diffusion coefficients suggest neuronal atrophy and glial activation/reaction in tumor core, respectively. Changes in intracellular and extracellular volume fractions and extracellular diffusivity quantified by DW-MRI support and complement metabolite DW-MRS result and further suggest a more infiltrative marging in IDH mutant tumors.    

Impact: Combination DW-MRI and DW-MRS has potential to characterize the glioma microenvironment for improved understanding of radio-resistance and developing more effective therapies. 

Keywords: Tumors (Pre-Treatment), Machine Learning/Artificial Intelligence, Fractal Dimension, Radiogenomic, Lacunarity, Glioma

Motivation: The presence of structural and geometric variations within gliomas, even among those with similar histologic grades, potentially reflect the phenotypic heterogeneity because of the distinct genetic and epigenetic landscape.

Goal(s): To develop a non-invasive radiogenomic platform to identify IDH and MGMT status using the geometry of glioma subcomponent.

Approach: Fractal dimension and Lacunarity, non-Euclidean geometric measures of glioma subcomponents, were estimated using MR images and wrapped in artificial intelligence-based models to discriminate IDH status and MGMT status.

Results: The combination of fractal dimension or lacunarity of enhancing and nonenhancing glioma subcomponent is the definitive discriminator of IDH status as wildtype or mutant.

Impact: Fractal Dimension and Lacunarity of Glioma subcomponents are unique for IDH-Mutant and IDH-Wildtype gliomas. Fractal-geometry analysis can serve as an effective non-invasive tool for identifying IDH-status prior to biopsy and surgical interventions, thereby improving the clinical management of glioma patients.

Keywords: Radiomics, Neuro, MGMT，habitat analysis，radiomics

Motivation: To predict the oxygen 6-methylguanine-DNA methyltransferase (MGMT) methylation status in high-grade gliomas (HGG) before surgery by using conventional MRI radiomics features within tumor habitat. 

Goal(s): To better understand the molecular characteristics of HGG.

Approach: In 105 HGG patients, the whole tumor was segmented into 3subregions by Kmeans clusters on T2 and T1 contrast-enhanced images. Radiomic features were extracted from each subregion and the predictive performance of radiomics signature was compared with clinical data.

Results: The efficiency of 3 subregions segmentation using Kmeans clustering with habitats analysis was the highest. The AUC of the model validation set was as high as 0.878.

Impact: We developed a radiomic signature model that can be used to predict MGMT methylation status in HGG patients. This can be used as a tool to help clinicians assess MGMT methylation status in HGG patients and guide individualized treatment.

Keywords: Tumors (Post-Treatment), Quantitative Imaging, Habitat imaging

Motivation: The lack of in vivo and noninvasive biomarkers to quantify tumor microenvironment (TME) normalization hinders the evaluation of bevacizumab (BEV) therapy response in glioblastoma (GBM). 

Goal(s): To quantify TME normalization during BEV therapy in GBM by conventional and multiparametric MRI (mpMRI).

Approach: The MRI-based habitats were generated by Gaussian mixture model in patient-derived GBM models. Spatial-paired analyses of MRI, histology, and single-cell RNA sequencing were performed to validate the effetiveness of habitats.

Results: A total of eight habitats were generated to quantify TME normalization spatiotemporally. Habitat7 was strongly correlated with TME normalization-associated phenotypes including pericyte coverage, hypoxia and immune cell infiltration.

Impact: We developed and validated a quantitative mpMRI-based biomarker to characterize TME normalization in GBM. This may provide a new in vivo approach for precise evaluation of BEV therapy response in GBM noninvasively.

Keywords: Alzheimer's Disease, Alzheimer's Disease, choroid plexus

Motivation: Dysfunction of the glymphatic system is one of the possible causes of Alzheimer’s disease (AD). We hypothesize that the choroid plexus (CP), the major site of CSF secretion, is associated with the hallmarks of AD, Aβ and tau protein deposition.

Goal(s): to investigate the association between CP and hallmark proteins in the AD.

Approach: Based on the proposed CP segmentation pipeline, univariate regression and stepwise regression models were employed to analyse correlations between CP and AD.

Results: Our work shows that the ratio between CP and parenchyma is correlated with Aβ42 and p-tau (p<0.001) and the CP volume is correlated with t-tau (p<0.001).

Impact: The proposed CP segmentation pipeline provided improved sensitivity to detect the correlations between CP/parenchyma ratio and Aβ42 and p-tau. This work may indicate the choroid plexus a possbile biomarker for AD.

Keywords: Other Neurodegeneration, DSC & DCE Perfusion, choroid plexus; dementia with Lewy bodies

Motivation: The choroid plexus has been demonstrated to play a significant role in the pathophysiology of dementia with Lewy bodies (DLB), however the imaging characteristics are not yet explored.

Goal(s): Our goal was to assess choroid plexus area and permeability based on MRI in DLB patients.

Approach: DLB patients were imaged to acquire choroid plexus area and permeability and compared with patients with Alzheimer’s disease and healthy controls.

Results: DLB patients exhibited larger area and lower fractional plasma volume in choroid plexus than healthy controls. Additionally, they were found to be significantly associated with the mini-mental state examination score.

Impact: The larger choroid plexus area and lower fractional plasma volume detected by T1-weighted MRI and DCE-MRI in dementia with Lewy bodies provide a non-invasive and quantitative metric for advancing the diagnosis and treatment of dementia with Lewy bodies.

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: Parkinson's Disease, High-Field MRI, Choroid Plexus, Synucleinopathy, RBD

Motivation: As opposed to Alzheimer's disease, the mechanism linking neurotoxic protein accumulation to alterations in neurofluid turnover and in neuroimmunity due to choroid plexus (ChP) changes is understudied in premanifest synucleinopathy.

Goal(s): To determine changes in the structure of ChP in premanifest synucleinopathy and to generate a ChP probabilistic atlas.

Approach: ChP in multi-contrast 7 Tesla images of 12 premanifest synucleinopathy and 12 sex-and-age-matched controls were evaluated in terms of volume and signal intensity.

Results: Reduced ChP volume in premanifest synucleinopathy suggested ChP atrophy that may result in neurofluid dynamics and neuroimmune function impairment; a probabilistic atlas of ChP was generated.  

Impact: ChP atrophy observed in premanifest synucleinopathy using high-resolution multi-contrast 7-Tesla MRI suggests a potential role of ChP in pathophysiology of synucleinopathies, urging further investigation. Probabilistic ChP atlas may aid precise MRI localization in future studies of ChP in living humans. 

Keywords: Multiple Sclerosis, Multiple Sclerosis, choroid plexus, neurodegeneration, neuroinflammation

Motivation: Conventional imaging limits the assessment of choroid plexus (ChP) inflammatory activity in multiple sclerosis (MS).

Goal(s): Use high-resolution MRI with 0.25mm² matrix at 7T to assess ChP changes in MS patients compared to controls and explore correlations with lesion volumes.

Approach: In 14 MS patients and 9 controls, ChPs were categorized into vascular and stromal compartments using 7T T2* imaging, and vessel-to-stroma ration was compared between the two groups.
 

Results: The ChP's vessel-to-stroma ratio quantified by 7T T2*w MRI was lower in MS patients, correlating negatively with lesion volume. Furthermore, the age-related decline was more rapid in MS patients compared to controls.

Impact: This pilot study suggests that the vessel-to-stroma ratio of ChP, as revealed by high-resolution 2D-GRE 7T T2* imaging, could potentially serve as an imaging marker for inflammatory and degenerative changes of ChP in MS patients.