Keywords: DWI/DTI/DKI, Ex-Vivo Applications, Post-mortem interval, sample size, fixation, human brain

Motivation: The ISMRM-Diffusion-Study-Group recommends a post-mortem interval (PMI) under six hours to avoid degeneration in ex-vivo tissue for validation of microstructure parameters estimated using preclinical MRI.  Fractional anisotropy (FA) deviation from the in-vivo value serves as a quality indicator.^[1]

Goal(s): Investigating the influence of PMI and tissue size on FA.

Approach: Five human whole-brains (PMI 15-24h) and a temporal-lobe (TL) specimen (PMI 2h) were examined with diffusion MRI (dMRI) before and after fixation.

Results: The FA of the unfixed whole-brain samples didn’t show differences to the in-vivo values, but between unfixed and fixed states. The FA of the TL specimen was unaffected during fixation.

Impact: For the PMIs examined here, myelin decomposition may not significantly affect FA from dMRI of unfixed post-mortem specimens. However, it can affect whole-brain samples during immersion fixation - an effect that may be mitigated by using smaller samples.

Keywords: DWI/DTI/DKI, Brain, Trans-membrane water exchange; FEXI; time-dependent DKI; in vivo

Motivation: Trans-membrane water exchange rate has been measured by several MRI methods but reported with largely variant results.

Goal(s): To explore whether Filter-exchange imaging (FEXI) and time-dependent DKI are comparable for water exchange measurements on the same subjects in human brain.

Approach: Eight healthy volunteers underwent FEXI and DKI(t) acquisitions on a 3T scanner. ROI-based analysis was performed to determine correlations between FEXI-derived AXR and DKI(t)-derived 1/τ_ex.

Results: A significant correlation between AXR and 1/τ_ex was found only in axial direction in white matter. This correlation should be interpreted cautiously because structural disorder has non-negligible effects on D(t) and K(t) in Kärger model.

Impact: While a significant correlation was observed between AXR and 1/τ_ex in the axial direction, this study suggests cautious use of DKI(t) for water exchange measurements due to potential deviations from the Kärger model's constant diffusivity assumption in the human brain.

Keywords: Simulation/Validation, Gray Matter, Software tools, Diffusion software, Monte Carlo

Motivation: Analytical diffusion models are limited in the complexity of brain tissue they capture, and creating complex numerical phantoms using meshes can be arduous. 

Goal(s): This work introduces an efficient and user-friendly software for generating realistic neurons using overlapping spheres.

Approach: Through Monte-Carlo simulations, we evaluate the impact of sphere overlap, soma-dendrites water exchange, and SNR on dMRI signals.

Results: The findings indicate that modest sphere overlap maintains signal quality. Additionally, the soma-dendrites water exchange has a significant  impact on the signal, even at realistic SNR. This work has promising implications for non-invasively quantifying gray matter microstructure, especially neuronal packing and cell membrane permeability.

Impact: Numerical phantoms of gray matter built with overlapping spheres offer an efficient and flexible way of studying water diffusion. This approach reduces the heavy meshing and cleaning process to trivial building blocks that can be adapted into realistic neuronal substrates.

Keywords: Diffusion Software, Diffusion/other diffusion imaging techniques, software, simulation, analysis

Motivation: Simulation and analysis of diffusion MRI (dMRI) in microstructures requires specialized expertise that limits its wide usage.

Goal(s): To provide an easy-to-use dMRI software package to enable end users to simulate and analyze diffusion in complex media.

Approach: MATI is written using object-oriented programming in two versions (MATLAB and Python) and can be used via a GUI or scripts. It can handle typical diffusion pulse sequences and various microstructures for data fitting, and arbitrary digitalized microstructures for simulating dMRI with GPU acceleration.

Results: A GPU-accelerated dMRI simulation and data fitting toolbox with a GUI has been developed for public use.

Impact: MATI, a GPU-accelerated toolbox for simulating and analyzing dMRI signals in various microstructures with a graphical user interface (GUI), has been developed. This easy-to-use package enables non-expert and expert users to simulate and analyze diffusion MRI signals in complex media.

Keywords: Simulation/Validation, Validation, White Matter, Microstructure, fODF

Motivation: Fiber ball imaging (FBI) makes quantitative predictions beyond the previously reported diffusion MRI (dMRI) b-value scaling of the direction-averaged signal.

Goal(s): Test the validity of the main FBI modeling assumptions.

Approach: In vivo human dMRI data from white matter were acquired at b-values ranging from 1000 to 10,000 s/mm², and the spherical harmonic expansion was calculated for degrees up to l = 6. Theoretical predictions from FBI for the harmonic power b-value dependence were then compared to the experimental results.

Results: A close matching is observed between theory and experiment for b ≥ 4000 s/mm².

Impact: The observed harmonic power b-value dependence strongly support the two main FBI assumptions that 1) axons can be modeled as thin, impermeable cylinders, and 2) intra-axonal water dominates the dMRI signal for b ≥ 4000 s/mm².

Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques

Motivation: To understand how axon density affects degeneracy-related errors in diffusion metric estimation.

Goal(s): Assuming ideal conditions (e.g., noise free and no inter-compartmental exchange), are there correlations between voxel contents and model degeneracies? 

Approach: A systematically-generated set of simulated voxels spanning a wide range of microstructural compositions, processed with various diffusion models, and analyzed both comparatively and component-wise.

Results: Based on the  individual signal component results, the extra-axonal water is the largest confounding factor. A model's depends largely on its ability to distinguish truly intra-axonal water from constrained extra-axonal water.

Impact: It is important to consider degeneracies when interpreting the results of diffusion modeling lest the associated errors propagate downstream. Our results offer a more robust understanding of the conditions which give rise to these degeneracy-related errors.

Keywords: Diffusion Modeling, Simulations, White-Matter, Monte-Carlo simulations.

Motivation: This work addresses the sampling bias related to voxel size and boundary conditions in DW-MRI Monte-Carlo simulations.

Goal(s): The study aims to understand how voxel-size and boundary-conditions influence Monte-Carlo simulations in DW-MRI to ensure convergence and minimise errors.

Approach: It uses simulations with realistic synthetic white-matter substrates and calculates diffusion propagators and apparent diffusion coefficients to measure simulation accuracy.

Results: It underscores the significance of voxel-size and boundary conditions in Monte-Carlo simulation and offers insights for better simulation parameters in DW-MRI. It also highlights conditions where errors can reach 20% and shows the need for larger voxel-sizes to achieve convergence.

Impact: The analysis focuses on making Monte-Carlo simulations reliable, enabling model validation from DW-MRI techniques. This has the potential to substantially improve microstructure assessment precision in clinical practice, enabling more accurate DW-MRI analysis.

Keywords: Diffusion Software, Software Tools, High-throughput, Docker, Reproducibility, Autism Spectrum Disorder, Brain Connectivity

Motivation: The complex whole brain connectome is underlined by white matter (WM) fibers featuring both long and short-range connections. Most existing protocols are tailored to trace long-range fibers, incapable of comprehensively tracing all fibers including both long and short-range fibers.

Goal(s): To achieve comprehensive mapping of whole-brain WM fibers in a unified framework including specifically high-fidelity tractogram of short-range fibers.

Approach: We developed DEPTH, a Docker-containerized pipeline extending advanced short-range fiber tracing.

Results: DEPTH offers high-throughput, reproducible and comprehensive tractogram of both long and short-range WM fibers. The Docker-based containerization ensures cross-platform usability and versatility without complex setup and configuration.

Impact: High-throughput, reproducible and comprehensive whole-brain white matter fibers across multisite datasets could now be traced by our software.

Keywords: Microstructure, Simulations

Motivation: The use of multi-exponential signal analysis is common practice to identify present diffusion components in diffusion-weighted MRI. Yet, the absence of appropriate acquisition parameters and standardised signal analysis methods hinders the attainment of accurate results, especially in renal imaging.

Goal(s): To assess the impact of fitting parameters and methods.

Approach: A simulation was conducted comparing non-negative (NNLS) and non-linear (NLLS) fitting methods, but also determining ideal parameters for accurate in-vivo appliance.

Results: The study showed superior accuracy when using the NNLS method in combination with area under curve (AUC) estimation and specified an optimized parameters set, improving clinical DWI examinations in kidneys.

Impact: By employing our results, it is expected that stable and reliable results can be achieved in multi-exponential analysis of in-vivo DWI data using both NNLS and NLLS approaches. This will enable enhanced evaluation of clinical DWI examinations in the kidney.

Keywords: Microstructure, Diffusion/other diffusion imaging techniques

Motivation: Time-dependent diffusion MRI, which is sensitive to non-Gaussian diffusion, reveals tissue microstructures and has been shown to improve cancer imaging and neuroimaging. However, gradient nonlinearity results in subject position-dependent bias for non-Gaussian diffusion characterization. Correction methods are needed.

Goal(s): To reduce the effect of gradient nonlinearity on 2D time-dependent diffusion MRI.

Approach: Slice-by-slice scaling of diffusion encoding gradients was applied to compensate for gradient nonlinearity.

Results: Uncorrected $$$\frac{ADC(60Hz)}{ADC(0Hz)}$$$ of a non-Gaussian diffusion phantom showed errors in off-center slices, where the actual diffusion gradient amplitude was reduced compared to prescribed values. The errors were reduced by prospectively increasing the prescribed diffusion gradient amplitude.

Impact: MR physicists, neuroimaging scientists, and radiologists, who are interested in microstructure imaging by probing time-dependent, non-Gaussian diffusion, will benefit from increased robustness to gradient nonlinearity and subject position, especially when using high-performance gradient systems that may have increased nonlinearity.

Keywords: Diffusion Reconstruction, Diffusion/other diffusion imaging techniques

Motivation: Real diffusion-weighted MRI (DWI) has shown improved diffusion contrast and more accurate estimation of diffusion parameters. However, current real DWI is still not optimal and its performance highly depends on the choice of parameters for phase correction, reducing its robustness, especially in body DWI.

Goal(s): To enable robust real DWI by improving phase correction and reducing noise floor

Approach: We combined deep-learning based phase correction and deep learning reconstruction to enable robust phase correction and to reduce noise floor.

Results: The phantom and healthy volunteer results demonstrated improved diffusion contrast in both acquired and synthesized high b-value neural and body DWI images.

Impact: DL-based phase correction and image reconstruction enables robust real DWI imaging, improving diffusion contrast and quantitative measurements in both neuro and body. This technique can be used to study cancer staging and treatment response in both low and high field.

Keywords: Diffusion Reconstruction, Data Processing

Motivation: Long scan times limit the clinical usage of diffusion MRI (dMRI)

Goal(s): We aim to perform rapid dMRI with high accuracy and reproducibility

Approach: We employ a Swin UNEt Transformers (Swin) model, trained on Human Connectome Project data and conditioned on registered T1 scans, to perform generalized dMRI denoising and super-resolution, requiring only 90 seconds of scan time.

Results: Compared with state-of-the-art self-supervised methods, the fully-supervised Swin UNETR achieved higher accuracy on external out-of-domain (OOD) datasets and exhibited 50% lower coefficient-of-variation for intracellular volume fraction and free water fraction measurements. Fine-tuning on even a single example scan improved performance.

Impact: Our approach achieves unprecedented accuracy and reproducibility in dMRI datasets acquired in different patient populations using different scanner models and pulse sequences and will enable much shorter dMRI scan times for patients unable to cooperate with lengthy imaging protocols.

Keywords: Diffusion Reconstruction, Diffusion/other diffusion imaging techniques

Motivation: Filter-based phase estimation requires tuning and is subject to the tradeoff between signal bias and vulnerability against phase inhomogeneity. DL-based phase correction has been shown to effectively remove both high- and low-frequency phase while minimizing signal bias.

Goal(s): To evaluate a DL-based phase correction method that improves the robustness of motion-induced phase estimation and its impact on noise and motion artifacts in MUSE reconstruction.

Approach: Volunteer brain and abdomen data were acquired with a MUSE sequence and reconstruction was performed offline.

Results: Compared to filter-based phase estimation, DL-based phase correction results in reduced noise and motion artifacts in MUSE reconstructed images.

Impact: MUSE enables high resolution DWI over a large FOV with reduced geometric distortion, but is very sensitive to shot-to-shot differences in motion-induced phase. DL-based phase correction can improve robustness in MUSE reconstruction, especially in anatomical regions with significant motion.

Keywords: Simulation/Validation, Software Tools, Phantom

Motivation: Building realistic and complex white matter numerical phantoms is needed for accurate diffusion MRI simulations but challenging to achieve.

Goal(s): The creation of white matter (WM) numerical phantoms by mimicking realisitic parallel axonal growth.

Approach: This tool uses overlapping spheres to build realistic axons and parallels their growth to decrease the running time while reliably preventing collisions.

Results: High intracellular volume fraction values can be reached (up to 70%) for tortuous, variable-caliber axons. The parallelism of the growth decreases the run-time.

Impact: Creating numerical phantoms that accurately represent white matter can improve the accuracy of results in diffusion MRI studies using Monte Carlo simulations.

Keywords: Microstructure, Microstructure, Time-dependent diffusion imaging; Oscillating gradient spin echo; Deep learning-based reconstruction

Motivation: Time-dependent diffusion MRI (td-dMRI) using oscillating gradient spin echo (OGSE) sequences is limited by low signal-to-noise ratio (SNR) and image quality.

Goal(s): To investigate the potential of combining OGSE sequences with deep learning-based reconstruction (DLR) to enhance image quality and precision of quantitative results in td-dMRI.

Approach: The OGSE-based td-dMRI images were reconstructed using both conventional method and DLR. The image SNR and quality, the precision of quantitative metrics and cellular-level microstructure maps without and with DLR were compared.

Results: DLR improved the SNR of images and ADC maps, eliminated Gibbs-ringing artifacts, and reduced singular values and outliers in cellular-level microstructure maps.

Impact: The combination of OGSE sequences with DLR shows promise in enhancing the image quality and quantification accuracy of td-dMRI. It may increase the feasibility and acceptance of the clinical application of OGSE-based td-dMRI.