Multidimensional MRI experiments have shown unique significance in resolving the correlations between physical parameters and relaxation properties. This study showed T₁ Relaxation-Diffusion correlation imaging spectra of an in vivo human brain and a multicomponent phantom with optimized acquisitions.  The acquisition paradigm was obtained from genetic algorithm (GA) by maximizing the singular values of the corresponding kernel functions. We detected significant differences in these spectra inversed from different components, which might help to identify tissue microstructure at the sub-voxel resolution. Our study offered a high probability of applying multidimensional MRI techniques in diagnosing neurological diseases.

Sources of MRI phase contrast include magnetic susceptibility, chemical exchange, and tissue microstructure. The novel DEEPOLE QUASAR method disentangles frequency sources and yields separate maps for magnetic susceptibility and non-susceptibility frequency shifts.

In this work, we validated the method in vivo and performed the first quantitative study of non-susceptibility frequency in the human brain. We found substantial non-susceptibility contributions in WM and GM. The quantification of non-susceptibility in the human brain provides new ground for theories on the origins of frequency contrast.

We have designed a novel QSM algorithm that addresses some of the limitations of existing techniques that combine the background removal and dipole inversion steps in a single step. We propose that the solution to the direct inversion problem can be aided by an iterative k-space algorithm and the inclusion of a priori information that represents feature-based and voxel-fidelity-based constraints. The considered approach, when compared with other techniques, resulted in a more accurate depiction of the susceptibility in high susceptibility deep gray matter (dGM) structures without sacrificing performance in regions like the cortex of the brain. 

Advances hyperpolarized ¹²⁹Xe production, data acquisition and image analysis have allowed ¹²⁹Xe MRI to emerge as a leading pulmonary imaging modality. As the number of sites capable of ¹²⁹Xe MRI and quantitative image-derived metrics grow, the need for an intuitive, organized analysis tool increases. Here, we provide a user-friendly application, designed as MATLAB language-based interface, to analyze HP ¹²⁹Xe data. This open-source, research tool enables standardized analysis of hyperpolarized ¹²⁹Xe calibration, ventilation, diffusion, and gas-exchange data. Furthermore, the modular design allows developers to easily implement additional functions and analysis procedures, providing a framework for growth and sharing of analysis techniques.

It is desirable to measure GABA, glutamate, and GSH in the thalamus for understanding pathological and pharmacokinetic changes. In this study, we study the feasibility of simultaneous measurements of these metabolites in the thalamus using HERMES. HERMES data were acquired on 20 healthy adult volunteers with and without saturation bands. HERMES data were processed in Gannet with and without spectral alignment. The results demonstrated the feasibility of using HERMES for simultaneous measurements of GABA, Glx and GSH in the thalamus. HERMES combined with saturation bands and retrospective spectral alignment yields high SNR spectra and reproducible measurements in the thalamus.

Obesity is associated with insulin resistance and chronic inflammation, both of which are known to affect skeletal muscle metabolism. However, studies in the impact of obesity on metabolism mainly focus on adipose tissue, instead of skeletal muscle. This study analyzes the in vivo pyruvate metabolism in the skeletal muscle of obese volunteers with hyperpolarized [1-¹³C]pyruvate. The results indicate increased lactate production and decreased pyruvate dehydrogenase activity in the skeletal muscle of obesity.

Thermometry using the proton resonance frequency (PRF) is challenging in inhomogeneous, fatty tissues and moving organs. Purpose of this study was to develop and test a combined free induction decay (FID)/spin-echo chemical shift imaging sequence for accurate hyperpolarized ¹²⁹Xe MR thermometry in rats exposed to hypothermia. Significant changes of ¹²⁹Xe lipid chemical shift and good agreement with core body temperature are observed. Bland–Altman analysis shows narrower limits of agreement and reduced mean difference for spin-echo acquisitions, suggesting improved precision and accuracy in fatty tissues. The method may be valuable as reference standard for development of more accurate ¹H MR thermometry.

An ultra-high diffusion b-value protocol (b=7,000-30,000s/mm²) was acquired in a high-performance 3.0 T MAGNUS head gradient system (max gradient amplitude=200 mT/m and max slew rate=500 T/m/s) for white matter microstructure imaging.With shorter TE enabled by MAGNUS, measurements of intra-axonal diffusivities are demonstrated at ultra-high b-values. At these ultra-high b-values, signal from extra-axonal water is effectively suppressed but SNR is also reduced. We show that real-valued image reconstruction maintains Gaussian noise properties and reduces Rician bias, allowing better quantification of intra-axonal diffusivities and effective axonal radii in healthy volunteers and in ongoing human subject research studies of mild traumatic brain injury.

Metallic implants create severe off-resonance adjacent to the implant, in the range of 2-20 kHz at 3T. The off-resonance pattern creates a static, “always on” background gradient with unknown amplitude and polarity. Static non-linear background gradients are a known source of local encoding errors, but their effect on diffusion contrast near implants has not yet been evaluated. Static background gradients are distinct from diffusion gradient non-linearities, which affect the achieved b-value if sufficiently large. We apply theoretical results and phantom experiments to evaluate two different diffusion encoding schemes and evaluate the effect of metal-induced off-resonance on ADC quantification.

LiverScope® is a novel point-of-care (POC) NMR technology for liver fat quantification. 

·       POC NMR PDFF agreed closely with ground-truth PDFF values in commercial PDFF phantoms (R² = 0.99)

·       POC NMR was feasible in an outpatient clinic, had no demonstrated adverse events, and agreed closely with contemporaneous MRI-PDFF reference standard values (R² = 0.94) in human adults with obesity and at risk for NAFLD: 

We optimize the sampling length parameter of Radial Diffusion Spectrum Imaging (RDSI) to improve fiber reconstruction accuracy of ODF-Fingerprinting (ODF-FP). As the ground truth, we use anisotropic diffusion phantoms containing taxons (textile water-filled tubes) with inside diameter of 0.8µm, approaching the anatomical scale of axons found in the human brain. Thanks to our optimized approach, ODF-FP reconstructs fibers crossing at angles as narrow as 10, 15, and 20 degrees with the average accuracy of approximately 10 degrees. We generalize our observations by proposing a criterion for selecting near-optimal sampling lengths to accelerate and simplify the application of ODF-FP with RDSI.

Although the voxel-wise signal acquired by diffusion MRI is symmetric, it is not always the case of the underlying fiber configurations. We present angle-aware bilateral filtering, an intuitive method for denoising fiber ODF fields revealing asymmetric fiber ODFs. To evaluate the effect of the filtering, tractography is performed on the resulting asymmetric fiber ODF field. Compared to the tractogram obtained from the original fiber ODF field, we show that using asymmetric filtered fiber ODF field as input to a fiber tracking algorithm reduces the ratio of incomplete streamlines and false connections and increases the proportion of true connections.

Time-dependent diffusivity is sensitive to microstructural geometry and has previously been described by a power-law scaling. However, the limits of validity of this relationship across different ranges of diffusion time are unclear.  We therefore acquired brain images of nonhuman primates in vivo with diffusion times from 1.9 to 40 ms to assess how well a power-law fits diffusivity in this range. We assess fitting in the time and frequency domains, whether pulsed (PGSE) and oscillating (OGSE) gradient spin echo can be combined for better results, and the sensitivity of diffusivity estimates to varying power-law exponents. 

Susceptibility separation enables a voxel-wise decomposition into paramagnetic and diamagnetic components. The repeatability of the method is examined in four structures of deep gray matter and three of white matter using scan rescan data of 22 subjects. The paramagnetic susceptibility separation is shown to be more robust than the diamagnetic and total susceptibility is shown to be similar to conventional QSM. Thus, susceptibility separation can provide both qualitative depiction of diamagnetic and paramagnetic susceptibility components and repeatable measures of paramagnetic components at least in deep grey matter.

Susceptibility maps reconstructed from small-axial slabs suffer underestimation due to background-field removal imperfections near slab boundaries and the increased difficulty of solving a 3D-inversion problem with reduced-support in the direction of B0. Reliable QSM reconstruction from small slabs would enable higher resolution imaging within a reasonable time-frame and further accelerate clinical adoption. This work proposed using additional rapid low-resolution data, that serves as prior information, to improve background-field estimation and regularize the inversion-to-susceptibility process. Consistent high-resolution QSM at 3T was obtained from slabs of width as small as 6.8mm, aided by a lower-resolution dataset of 16 times coarser voxels.

J-resolved 1H-MRSI offers several unique advantages but suffers from long acquisition time and lmited SNR, especially for longer TEs. Leveraging the recent progress on constrained MRSI reconstruction using learned nonlinear low-dimensional representations, we propose here a new method for SNR-enhancing reconstruction from rapidly generated, noisy J-resolved data, that can computationally efficiently enforce an accurate nonlinear low-dimensional representation of high-dimensional J-resolved spectroscopic signals through a learned network-based project as well as complementary spatial constraints. The proposed method has been shown to improve the SNR significantly for in vivo J-resolved 1H-MRSI of the brain.

Frequency drift from EPI-based sequences prior to MRS scans is a known issue. Running MRS scans ‘from cold’ is suggested for the greatest scanner stability, with some equilibration period for systems with overnight energy-saving mode. Running MRS scans before scanner reaches an equilibration period can result in large frequency drifts. Under these conditions, the effect of running fMRI scans seem to be negligible. Applying frequency and phase correction can make the final spectrum appear to be qualitatively similar between scans with big and negligible drift.  

We proposed a fast 3D CEST imaging method using compressed sensing SPACE and demonstrated its routine clinical application for simultaneous APT, ssMT, and rNOE imaging. The total scan time of CEST images with 58 z-spectra and an off-resonance map was 6 minutes and 49 seconds. We used low refocusing flip angle and high bandwidth to achieve a narrow point spread function, and the images could be reproduced between different scans. This method could be used for tumor grading and treatment response assessment at 3T.

Direct quantification of cerebral metabolic rate of oxygen consumption (CMRO₂) is possible with dynamic ¹⁷O-MRI after inhalation of isotope-enriched ¹⁷O₂. In this work, we investigated oxygen metabolism in posterior fossa tissue including cerebellum, pons and midbrain. The determined CMRO₂ values in these areas were in good agreement with literature values obtained by ¹⁵O-PET studies. Our results demonstrate that dynamic ¹⁷O-MRI may provide a valuable tool for direct measurement of CMRO₂ in posterior fossa tissue.

Detecting dopamine in vivo is critical for understanding many neurologic and psychiatric disorders. In this work, we developed a paramagnetic CEST-MRI method for dopamine detection using NaHoF₄@DBA nanoparticles. Our results showed that the dopamine in mouse brain can be selectively enriched in the surface of the nanoparticles and detected by CEST-MRI. Moreover, we found that the magnitude of ST% map reflecting dopamine levels in the brain of SZ mice was relatively higher than that of PD mice. 

This novel study explores amide proton transfer weighted (APTw) imaging in people with relapsing-remitting multiple sclerosis (pw-RRMS). We evaluated the APTw signal intensity in selected MS lesions and normal-appearing white matter (NAMW) regions in 9 pw-RRMS. Compared to NAWM regions, a statistically significant increase in APTw signal intensity was observed in the MS lesions. Elevated APTw signal intensity could mark increased mobile myelin proteins decomposition and accumulation from the demyelination process.

Chemical exchange saturation transfer (CEST) imaging shows great potentials in the diagnosis of brain diseases. However, there are still many challenges in the hepatic disease due to the fat suppression and large B₀ inhomogeneity. In this study, we achieved CEST imaging with simultaneous B₀ correction in one sequence, and conducted a pixel-by-pixel prediction on the background reference Z-spectra representing the magnetization transfer and direct saturation effects. Our approach also effectively separated the amide proton transfer (APT) effects from the mixing effect of fat and nuclear Overhauser enhancement (NOE), by subtracting the background reference Z-spectra from the predicted Z-spectra.

APT MRI scans are often performed under non-equilibrium conditions, confounding imaging quantification and tissue characterization. This study proposed a quasi-steady-state (QUASS) algorithm for fast and accurate tumor APT imaging. Seven healthy volunteers and nineteen tumor patients were scanned at 3T, with two representative RF saturation times (Ts) and relaxation delays (Td). The routine APT measures significantly varied with Ts and Td (P<.001) and were significantly smaller than the corresponding QUASS indices (P<.001). In contrast, QUASS reconstruction results showed little dependence on scan protocols (P>.05). The QUASS algorithm enables robust APT imaging, promising to expedite and standardize clinical APT tumor MRI.

The identification of the isocitrate dehydrogenase mutant (IDH_MUT) glioma and IDH wild-type (IDH_WT) glioma has significant diagnostic, prognostic, and therapeutic implications. In this work, we evaluated the non-invasive prediction of IDH mutation status in glioma mice by employing a combination of high spectral resolution CEST MRI at 11.7T together with data analysis using machine learning approaches. We demonstrated that in vivo CEST signals correspond very well with glioma genotypes. Among several CEST signals, the signals at Δω =3.5 ppm may serve as the major indicator to differentiate the IDH genotype glioma from other types.

Single reference variable flip angle T1 mapping is subject to some remaining bias due to T2* effects between baseline and dynamic acquisitions. Previous work determined a computationally expensive correction for this bias. In this work, we present a much simpler extrapolation method to correct for T2* related bias in T1. This study provides an analysis through simulation of the benefits and drawbacks of this simpler extrapolation correction compared to the previous correction for bias on the SR-VFA T1 calculation.

Laser interstitial thermotherapy (LITT) is a clinical treatment modality performed under the guidance of magnetic resonance temperature imaging (MRTI). Current limitations in MRTI prevent real-time volumetric monitoring of the tissue regions being treated. In this work, we investigate the use of the model predictive filtering method (MPF), using a Green’s function heat kernel, to reconstruct large field-of-view MRTI data of phantom heating to validate their use for future real-time volumetric MRTI treatment monitoring.

We propose a new calibrated fMRI approach for non-invasive measurement of the cerebral metabolic rate of oxygen of oxygen consumption and the deoxyhaemoglobin weighted blood volume. The method exploits the differential dependence of the gradient and spin echo BOLD signals on OEF. Unlike conventional calibrated approaches, we show that gradient-spin echo calibrated fMRI requires only a single respiratory modulation to determine OEF. The method is applied in-vivo using a repeated breath-holding protocol, and initial results show agreement with global measurements of oxygen saturation.

This work presents a multi vendor multi-site MRI study in which a TRUST sequence was harmonized across three MRI platforms from GE, Siemens, and Philips to measure the cerebral venous oxygenation Y_v. We carried out intra-scanner and inter-scanner analysis on the variability of the venous oxygenation measurements and demonstrated high measurements reproducibility across the three platforms.  Moreover, we examined the relationship between the fluctuations in end-tidal CO₂ and the Y_v measurements and showed that end-tidal CO2 can reduce the variability in Yv measurements in multi-site setting.

In this study, we used a newly developed tri-frequency RF surface coil that enables ¹H MRI and interleaved ²H/¹⁷O MRS measurements to simultaneously assess cerebral glucose and oxygen metabolism, cerebral blood flow and oxygen extraction fraction in rat brain at 16.4T with administration of ²H-labeled glucose and ¹⁷O-labeled oxygen gas. Our results demonstrate that this approach can capture the activities of major pathways relevant to glucose and oxygen metabolism and perfusion in the same brain at the same time, thereby provide comprehensive information crucial for understanding the metabolic and vascular coupling or uncoupling relationship in healthy and diseased brains. 

Hyperpolarized [1-¹³C₁]pyruvate MRI data were obtained in vivo metabolic data from young and aged animals in rodent to elucidate the different changes in metabolism due to aging. The preliminary results in this study demonstrated the feasibility of using hyperpolarized MRI ¹³C technique for identifying renal metabolic changes between aged and young animals. The reduction of Lac/Pyr, Ala/Pyr and BiC/Pyr ratio may be used as biomarkers for the cellular dysfunction that accompanies the aging process. The results from this study warrant further research on the usage of hyperpolarized ¹³C MRI in monitoring metabolic changes in aging-related studies.

A multi-b-value DWI imaging method based on multi-shot EPIK with continuously changing b values and Locally Low-Rank (LLR) constraint is proposed. The multi-shot EPIK acquisition improves resolution of multi-b-value DWI, and acquisition of the central k-space in every b-value bypasses the need for careful initialization. The LLR constrained problem is solved by a newly developed ADMM algorithm with robust performance. Preliminary data in healthy subjects showed that the proposed method outperforms LLR-POCS and SPA-LLR for reconstruction at high undersampling rates. 

The use of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) has increased for renal function and fibrosis assessments. However, debates have continued about which method is best. In this study, we used IVIM to assess the pathologic injury associated with IgA nephropathy and compared the results with the mono-exponential model of DWI. Both diffusion models were derived from advanced zoomed field of view (FOV) DWI acquisitions. IVIM had better diagnostic performance than the mono-exponential model in assessing the renal pathologic injury in patients with IgA nephropathy.

Diffusion tensor imaging (DTI) requires several diffusion-weighted image (DWI) acquisitions, leading to a long scan time. In this study, a deep-learning model based on multi-slice information sharing was implemented to obtain ultrafast DTI. This method uses the similarity of DWIs among neighboring slices to minimize the requirement of the number of diffusion-encoding directions. The results indicate that the proposed method can reconstruct high-quality diffusion and DTI-derived maps, exceptionally robust to noise in fractional anisotropy mapping even when only 3-direction DWIs.

Magnetic Resonance Electrical Property Tomography (MREPT) could provide important contrast for non-calcified tumors. However, MREPT relies on numerical differentiation, which is noise sensitive and prone to artifacts near boundaries.

In this work, physics-informed neural networks (PINN), NN empowered automatic differentiation is proposed to improve MREPT by mitigating artifacts and reducing noise sensitivity. Instead of calculating partial derivatives numerically, is obtained by backpropagation through PINNs.

For clinical MREPT, reduction of ground-truth information to guide PINNs was investigated. Results show that above 25% collocation points, reconstruction can be made at 100 SNR. PINNs enable noise-robust and artifact-free MREPT from less ground-truth information.

Cellular microstructural parameters mapping can provide quantitative information for tumor diagnosis and treatment monitoring. Particularly, voxel-wise cell size distribution may provide critical clinical biomarkers to characterize heterogeneity of tumors. Currently, MRI-cytometry may measure such a distribution, but it “blurs” the distribution peaks and prevents differentiating different cell populations with different cell sizes. In this work, we develop a new approach to improve this and hence make it more practical to distinguish different cells. Detection of T cell infiltration for assessment of early response to immunotherapy may be a potential application of this method.

Four-dimensional diffusion-weighted imaging (4D-DWI) attracts lots of attention in MRI-guided radiotherapy since it can provide functional information and better delineate the tumor. However, the previously reported 4D-DW-Propeller-EPI technique used amplitude data binning with continuous interval to sort the blade data that might lead to non-uniform distribution of k-space sampling, therefore being suboptimal for subsequent Propeller-EPI reconstruction. Thus, we propose a new (K-B-optimized) binning method to consider the requirement of propeller reconstruction during the sorting of blade data, thereby improving the k-space distribution. Simulation and in-vivo studies demonstrated that the new binning method can improve the data quality of 4D-DW-Propeller-EPI.

We investigated the characteristics of diffusion quantification values in a restricted structure with a capillary phantom in PGSTE and OGSE. Smaller structure sizes require shorter diffusion times for structure estimation. In OGSE, we observed a transition of diffusion coefficients under 10 ms for capillary sizes from 6-12 μm, but no differences for those from 25-100 μm. Moreover, it was necessary to consider the temperature dependence of the diffusion measurement at low temperatures. We developed a system to measure the effects on diffusion quantification values at three phases of different temperatures, structure sizes, and diffusion times.

Diffusion MRI provides opportunities in probing tissue microstructure based on mm-scale measurements. Generally, an MRI diffusion model is used to infer biologically meaningful information from diffusion MRI measurements. Recently, there has been increasing interest in mapping the so-called dot-compartment in the human brain. The dot-compartment has been ascribed to a tissue compartment which has highly restricted diffusion. Using a custom diffusion MRI line scan we investigated the ability to obtain high b-value signals using 3T MRI equipped with 80mT/m gradients. It appears that the dot-compartment is elusive when probed using clinical standard scanners even when b-values of 15000s/mm² are achieved.

Quantitative microstructural imaging based on diffusion MRI usually replies on some simple gradient waveforms, with which analytical expressions can be derived e.g., for fitting cell size. However, it is challenging for this approach for modified irregular gradient waveforms that are increasingly used. Inspired by Callaghan’s matrix formalism, we propose an efficient approach for signal computation with arbitrary gradient waveforms. It can accelerate computation by three orders of magnitude with maintained accuracy, making it feasible in practical data fittings. This work paves the way for quantitative microstructural imaging with arbitrary diffusion gradient waveforms in practice. 

Quantitative microstructural imaging based on diffusion MRI provides a non-invasive means to measure microstructural parameters, e.g., cell size. However, due to the remarkable complexity of incorporating transcytolemmal water exchange into diffusion biophysical models, water exchange is usually ignored. This leads to biased estimation of microstructural parameters, particularly intracellular volume fraction. Here, we propose a new approach to incorporate water exchange naturally in diffusion biophysical models with arbitrary gradient waveforms, making it possible to fit cell size, density, and intracellular water lifetime simultaneously. This establishes a general framework to incorporate transcytolemmal water exchange in any diffusion MRI-based microstructural imaging models.