Keywords: Microstructure, Brain

Motivation: Diffusion MRI is a promising means to map mesoscopic human brain architecture in vivo, however an appropriate model is required that relates the MR signal and features of the underlying microstructure within the voxel.

Goal(s): The goal of this study is to determine whether a Gaussian diffusion model is applicable at the mesoscale.

Approach: We compared single and double diffusion encoded signals acquired with large b-values at two different diffusion times to test for Gaussianity.

Results: We found no significant time-dependence in the diffusion weighted signals in brain parenchyma, confirming the applicability of the Gaussian diffusion at the mesoscale.

Impact: The study resolves the ongoing debate on the appropriate model to use to analyze the diffusion weighted signals in live human brain at clinically accessible spatiotemporal scales. 

Keywords: MR-Guided Interventions, MR-Guided Interventions, Irreversible Electroporation

Motivation: Treatment monitoring of irreversible electroporation (IRE) is only feasible on small animal MRI systems.

Goal(s): Demonstrate Current Density Imaging capabilities of the two-shot CP/CPMG-RARE with spiral sampling by monitoring IRE of a potato in a clinical MR scanner. 

Approach: A potato is irreversibly electroporated, while the two-shot CP/CPMG-RARE sequence is acquiring. The electric field is simulated, and the current density is projected using the MRCI toolbox. T1 maps are calculated from inversion recovery sequences.

Results: Simulated and projected current density fields are in good agreement. IRE monitoring in single slices is feasible in clinical MR scanners in optimal conditions.

Impact: Irreversible Electroporation is a promising non-thermal ablation therapy without in-situ validation available. This work presents a step towards implementing treatment monitoring on clinical MR scanners utilizing a two-shot CP/CPMG-RARE with spiral sampling as Current Density Imaging sequence. 

Keywords: Microstructure, Microstructure

Motivation: Tractometry is used to estimate the microstructural properties of white matter tracts from volumetric images. However, it has significant limitations due to multi-fiber voxels that bias tract measurements.

Goal(s): We aim to estimate the tract-specific g-ratio, a ratio of the inner and outer radius of the myelin sheath.

Approach: Building on the COMMIT framework, we disentangle the microstructural features of individual white matter tracts to estimate tract-specific g-ratio.

Results: Tract-specific g-ratio had higher contrast between tracts and had a stronger correlation with tract caliber (i.e. the axonal cross-sectional area between two nodes derived from COMMIT) and length in comparison to tractometry.

Impact: By using this novel COMMIT-based pipeline to analyze diffusion and myelin-sensitive MRI data, we anticipate that tract-specific g-ratio measures will be more sensitive to subtle differences in g-ratio across tracts and individuals due to the elimination of partial volume effects.

Keywords: Fat & Fat/Water Separation, Fat, Deep Learning, 3D Convolutional Neural Network

Motivation: The fast spin-echo triple-echo Dixon images acquired with flexible echo-spacing (FTED-Flex) can be used to generate separated water and fat images with enhanced T2-weighted contrast. However, their performance and clinical applications are limited by its long image reconstruction time.

Goal(s): Our goal is to develop a fast and accurate FTED-Flex image reconstruction method.

Approach: The time-consuming phase estimation was replaced by a 3D deep-learning neural network.

Results: The FTED-Flex integrated with a 3D deep-learning network was highly accurate (average Dice coefficient in volume-of-interest=0.989) and reduced the processing time for phase-estimation to a few seconds, compared to tens of minutes by conventional methods.

Impact: The developed FTED-Flex integrated with a 3D deep-learning network is highly accurate and reduces the processing time for phase-estimation to a few seconds, thus it has a great potential to expand clinical applications of FTED-Flex imaging.

Keywords: Multi-Contrast, Contrast Mechanisms

Motivation:  Quantitative biomarkers such as proton density, longitudinal- and transverse relaxation rates, and magnetization transfer saturation measured by multi-parameter mapping (MPM) reflect microstructural tissue characteristics. However, resolution and reproducibility of MPM are constrained by SNR limits of the imaging process resulting in long acquisition times.

Goal(s): We investigate tMPPCA denoising performance for improved SNR and reproducibility of quantitative maps at different voxel volumes for a clinically optimized protocol.

Approach: Denoising of repeated MPM acquisitions at different voxel volumes and its effects on model-based SNR and reproducibility of parameter maps.

Results: Denoising increase SNR of quantitative maps up to sixfold while scan-rescan fluctuations were halved.

Impact: Tensor-based MPPCA denoising of multi-contrast images enhances SNR of quantitative Multi-Parameter Mapping which results in improved reproducibility with greater benefits for high-resolution applications. Consequently, tMPPCA denoising could be considered for future studies and for retrospectively enhancing sensitivity of MPM studies.

Keywords: Microstructure, White Matter, Histology, Axon Radius Distribution, Intra-axonal signal, Rician bias

Motivation: To understand deviations between axon radii from in vivo MR experiments and histology.

Goal(s): To assess the sensitivity of the intra-axonal MR signal to the axon radius; to assess the impact of confounders (extra-axonal signal and Rician noise bias); to discuss deviations between in vivo MR experiments and simulations.

Approach: We simulated MR signals for axon radii distributions from large-scale histology with and without confounders; we compared radii fitted to in vivo MR experiments and simulations.

Results: Large MR radii are inherently underestimated; confounders are expected to further bias MR radii and can potentially explain deviations between in vivo MR experiments and simulations.

Impact: We reveal an inherent bias in the MR axon radius model for in vivo measurements. Furthermore, we identified two main confounders that can significantly narrow the dynamic range of MR radius measurements and reduce sensitivity to small-axon radii regions.

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

Motivation: Bright fat signals in MR images can obscure the underlying details and affect the physician's diagnosis. Especially in ultra-low-field MRI (B₀ < 100 mT), lower SNR images are more important to discern fat and water signals.

Goal(s): This study aims to achieve effective water-fat separation using the Dixon method at a 50 mT MR scanner.

Approach: R₂^* effect and priori information have been added to the existing two-point Dixon method. 

Results: The images obtained on the 50 mT MRI scanner can clearly distinguish cartilage, muscle and fat compared to the water-fat separation images obtained on the 3T MRI scanner.

Impact: Phase errors in the acquired MR images were significantly reduced after using priori phantom phase images. This work demonstrates the successful application of the Dixon method to ULF MRI. Future studies will focus on reducing imaging time.

Keywords: Fat & Fat/Water Separation, Fat

Motivation: Despite recent advances, chemical shift-encoded MRI (CSE-MRI) remains a challenging problem and many algorithms are computationally expensive, leading to interest in deep learning-based methods. However, initial attempts have used convolutional neural networks (CNNs), which are limited by data requirements, poor generalisability across different anatomies (‘anatomy-dependence’) and training time.

Goal(s): To address these limitations, we propose a deep learning-based method known as RAIDER.

Approach: RAIDER uses two multilayer perceptrons (MLPs), each trained separately with simulated single-voxel data, to achieve ultrafast parameter estimation.

Results: RAIDER is several orders of magnitude faster than conventional fitting, with similar/better performance, and avoids the inherent limitations of CNN-based methods.

Impact: RAIDER delivers ‘ultrafast’ CSE-MRI processing whilst avoiding the data and training-time requirements and anatomy-dependence of CNN-based methods. It could simplify, accelerate and reduce the cost of CSE-MRI processing in both research and clinical practice.

Keywords: Multi-Contrast, Spectroscopy, White Matter, MR Fingerprinting, Relaxometry

Motivation: Accurate ¹H-MRS metabolite quantification requires adjustments for metabolite and water signal relaxation, which are challenging to measure.

Goal(s): Our goal was to examine whether an MRF-based correction of subject-specific water relaxation times, applied to patients with mild traumatic brain injury (mTBI), yields results and effect sizes comparable with a conventional literature-based correction approach that utilizes one set of relaxation times for all subjects.

Approach: MRF and ¹H-MRSI were acquired in 21 mTBI patients and 20 age-matched controls for quantification of metabolite concentrations in six white matter regions.

Results: Both methods yielded similar findings with comparable effect sizes across all metabolites in all regions.

Impact: In the context of intermediate TR and short TE, the standard absolute quantification method based on one literature-derived set of water relaxation times for all subjects may be appropriate for studying white matter metabolism in mild traumatic brain injury.

Keywords: Multi-Contrast, Multi-Contrast, MR histology, multi-parametric MRI, deep learning, diffusion, magnetization transfer, relaxivity, mouse brain

Motivation: Deep neural networks trained with MRI and myelin histology data offer enhanced sensitivity and specificity compared to conventional MRI markers, yet their inner workings remain unknown. 

Goal(s): To elucidate the relationships between MRI and myelin histology.

Approach: We mapped multi-parametric MRI data of developing mouse brains and their myelin content onto a 3D manifold after dimension reduction and defined the relationships between MRI and myelin signals in a piecewise fashion.

Results: Our findings revealed how the relationships between multiple MRI parameters and tissue myelin content evolved throughout brain development. 
 

Impact: We have developed a novel data-driven approach to characterize the complex relationship between MRI parameters and myelin. The results suggest that multi-parametric MRI is necessary for accurate myelin mapping. 

Keywords: Multi-Contrast, Modelling, Fixation, Postmortem whole human brain, Hydration

Motivation: Relaxation rates in the in-vivo human brain are strongly different to their counterparts in formalin-fixed postmortem tissue.

Goal(s): To model the changes of the relaxation rate parameters for different tissue stages from in-vivo to ex-vivo: unfixed, during fixation and during hydration.

Approach: The multi-parameter mapping (MPM) protocol was used to measure the changes of five whole-human brains across the aforementioned tissue stages, and different saturation models were tested to describe relaxation parameter changes during fixation.

Results: The MPM parameters varied strongly per tissue stage, and a mathematical description of the change of the MPM during fixation was found.

Impact: We characterised the MPM parameters during the fixation and hydration process across the entire brain and propose a mathematical model to describe the changes. This information could facilitate translating microstructure-mapping methods from fixed ex-vivo tissue samples to in-vivo application

Keywords: Fat & Fat/Water Separation, Fat

Motivation: Reliable water-fat separation and quantification are of critical importance in the MRI assessment of diseases involving metabolic disruption and fat infiltration.

Goal(s): To develop a model-free approach for spectrally selective and interleaved water imaging and fat Imaging (siWIFI).

Approach: We designed a new sequence that selectively acquires water and fat signals in an interleaved fashion. This new sequence was tested on phantoms and healthy subjects.

Results: The measured fat fraction showed excellent correlation with fat concentrations of phantoms. Both phantom and healthy subject images were comparable to those from standard IDEAL scans. 

Impact: Our new method, termed siWIFI, selectively images water and fat for water-fat quantification which does not require complicated post-processing. Combining with MT preparation shows the feasibility of simultaneous quantification of fat infiltration and fibrosis development.

Keywords: Multi-Contrast, Ex-Vivo Applications, Ischemia, Microstructure, Relaxometry, Screening, Tissue Characterisation, Traumatic Brain Injury

Motivation: Detection of brain edema at forensic examination remains subjective and observer dependent, but more objective criteria perform poorly. A notable exception is the normalized brain weight.

Goal(s): Investigate MRI measures of death-associated edema.

Approach: We establish in situ water content mapping and T2* relaxometry in a pilot study, adapting a fast quantitative protocol (~6min) using a standard mGRE sequence.

Results: Using the derived quantitative maps, we find correlations between water content and T2* in WM,  between tissue water weight and brain weight, and macromolecular density vs normalized brain weight. Microstructural characterization of brain oedema with qMRI seems feasible.

Impact: Assessing the presence of edema as indicative of the cause of death is important for forensic examinations but is currently observer dependent. We seek to establish objective qMRI-based diagnostic measures and propose microstructural markers such as the macromolecular density.

Keywords: Fat & Fat/Water Separation, Contrast Mechanisms

Motivation: MR Z-spectral imaging (ZSI) offers a new way to generate fat spectrum. However, its feasibility for fatty acid characterization remains to be elucidated.

Goal(s): This study designed a ZSI protocol and investigated its capability in fat measurement.

Approach: The designed ZSI protocol was tested on a fat-water phantom and vertebral body marrows in healthy volunteers and osteoporosis patients at 3 T. 

Results: ZSI-measured fat fraction (FF) significantly correlated with oil volumes in the phantom. Moreover, osteoporosis patients exhibited significantly higher normalized fat peak amplitudes and FF than healthy volunteers, indicating the ability of ZSI in revealing fatty acid differences under different pathological states.

Impact: The designed ZSI protocol was feasible for fatty acid characterization. Significant differences of fatty acid metrics were detected between osteoporosis patients and healthy volunteers, suggesting the potentials of the designed ZSI protocol in facilitating fat-related disease diagnosis and evaluation.

Keywords: Fat & Fat/Water Separation, Fat

Motivation: This research explores using multiple bSSFP images to generate arbitrary spectral profiles for use in water-fat separation

Goal(s): Our goal is to analyze the generation of synthetic spectral profiles and use this technique for water-fat separation.

Approach: We use computational simulations to analyze the effectiveness of generating arbitrary spectral profiles for filtering different tissues and validate our technique with an in-vivo knee experiment.

Results: Our experiments demonstrate that the banding artifacts from multiple bSSFP acquisitions with varying TRs and phase cycles can be used to spectrally isolate water from fat signals.

Impact: Multiple bSSFP images can be linearly combined to generate arbitrary spectral profiles and isolate tissues for water-fat separation

Keywords: Fat & Fat/Water Separation, Spinal Cord, Gradients, Head & Neck /ENT, Parallel Transmission & Multiband, Simulations, System Imperfections: Measurement & Correction

Motivation: Insufficient fat saturation compromises image quality in clinical examinations.

Goal(s): To improve the quality of spectral fat saturation resulting in less residual fat signal in the acquired image.

Approach: Individual 4D spectral-spatial pulses based on subject-specific field maps and a numerically found trajectory are designed within an online workflow. A universal RF solution is also calculated. Performance is compared to Gaussian and SLR pulses on ten cervical spine datasets and one in-vivo measurement.

Results: Simulations show significantly improved fat saturation with individual and universal spectral-spatial pulses, while average water excitation remains low only for individual pulses. The in‑vivo measurement supports the simulation results. 

Impact: Customized and universal spectral-spatial fat saturation pulses outperform currently used spectral pre-saturation pulses enabling more definitive interpretation of fat‑suppressed MR images. Potential application to a variety of sequences is straightforward by replacing the pre-saturation pulse with our design.