Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Diffusion acquisition

Motivation: Diffusion-weighted steady-state free precession (DW-SSFP) has demonstrated higher SNR-efficiency vs the diffusion-weighted spin-echo (DW-SE) in post-mortem tissue. However, its sensitivity to microstructural features has not been comprehensively investigated.

Goal(s): To develop an investigation framework to quantify DW-SSFP’s sensitivity to microstructure.

Approach: We combined Monte-Carlo simulations with phantom experiments incorporating diffusion hinderance/restriction and an ex-vivo mouse brain, comparing the estimated diffusion attenuation of DW-SSFP vs a DW-SE sequence with matched gradient waveforms and diffusion timings.

Results: DW-SSFP exhibited higher diffusion attenuation vs DW-SE in all tested substrates when gradient waveforms/timings are matched, suggesting its unique signal forming mechanisms may be highly sensitive to microstructure. 

Impact: We present a framework combining Monte-Carlo simulations with experiments to characterise the sensitivity of diffusion-weighted steady-state free precession (DW-SSFP) to microstructure. DW-SSFP demonstrates greater signal attenuation versus a gradient waveform/timing-matched DW-SE across different substrates, demonstrating its potential for microstructural imaging. 

Keywords: Diffusion Modeling, Modelling, strain, diffusion, permeability, random walk, finite volume

Motivation: To investigate the effects of strain on diffusion where existing studies are limited to isotropic unrestricted media.

Goal(s): The aim of this study is to develop and validate a novel methodology that extends the classic Monte Carlo random walk algorithm to incorporate the effects of strain within complex media.

Approach: Strain is included in the MCRW simulations displacing the geometry and the particles in each time step and is validated against established finite volume (FV) methods and an analytical solution for free diffusion.

Results: We demonstrate the ability to assess changes in the diffusion tensor due to cyclical strain and long diffusion times.

Impact: The updated MCRW model offers new capabilities for quantifying strain-induced biases in diffusion tensor CMR metrics enabling clinicians to more accurately interpret microstructural changes, particularly in patients with pathological alterations.

Keywords: Simulation/Validation, Perfusion, cancer, IVIM, microvasculature

Motivation: Open-source software for simulating diffusion MRI (dMRI) signals arising from micro-vascular perfusion is needed to inform the development of new techniques for non-invasive vascular characterization.  

Goal(s): To present FlowSim, a micro-vasculature perfusion dMRI signal simulator, demonstrating its utility for in vivo vascular property estimation.

Approach: FlowSim estimates blood velocities in all segments of custom vascular networks. These are used to calculate spin trajectories in the presence of arbitrary diffusion-encoding gradients.  

Results: FlowSim synthesizes dMRI signals from realistic vascular networks reconstructed from histology. These can be used to inform the estimation of capillary blood velocity distributions in vivo, showcased herein in cancer.  

Impact: We present FlowSim, a simulator of diffusion MRI (dMRI) signals arising from micro-vasculature perfusion. FlowSim synthesizes dMRI signals from realistic vascular networks reconstructed from histology, and informs the estimation of new microvasculature metrics in vivo, needed, for example, in cancer.

Keywords: DWI/DTI/DKI, Diffusion Tensor Imaging

Motivation: Is low b-value diffusion tensor imaging (low-b DTI) useful for analyzing cerebrospinal fluid flow, which is a slow and complex flow that is difficult to measure?

Goal(s): To validate low-b DTI for analyzing the slow flow.

Approach: To compare low-b DTI with 4D-flow in the fluid phantom where water flowed at a constant slow rate.

Results: A strong correlation between the mean diffusivity of Low-b DTI and the mean square of deviation of velocity of 4D-flow, which is supported by the theory that these correlate in laminar flow.

Impact: If low b-value diffusion tensor imaging is useful for analyzing slow and complex flows of cerebrospinal fluid, it should contribute to understanding the pathogenesis of various diseases in which impaired cerebrospinal fluid clearance system may be part of the etiology.

Keywords: Simulation/Validation, Validation, Prostate cancer diffusion VERDICT

Motivation: Intracellular volume fraction (fIC) maps from VERDICT-MRI have shown potential to improve prostate cancer (PCa) stratification, but the microstructural origin of the signal has not yet been investigated in in vivo settings.

Goal(s): Investigate the accuracy of fIC from in vivo VERDICT-MRI as a measurement of cell density using matched prostatectomy specimens.

Approach: Using personalised moulds from multiparametric (mp)MRI and deep learning image registration, we align whole prostatectomy histology images with corresponding VERDICT MR images. We compare fIC maps against cell density maps derived from histology.

Results: fIC maps show very strong agreement with histology-derived cell density maps of epithelial cells (r=0.8303).

Impact: Our study shows that VERDICT fIC maps are accurate descriptors of epithelial cell density in the prostate. The biological interpretability of these maps will facilitate translation into clinical practice, improving PCa stratification from MRI.

Keywords: Microstructure, Microstructure

Motivation: Improving parameter estimation for the standard model of diffusion in white matter (SM) by modelling the subsequent decay of the spin-echo of the dMRI signal.

Goal(s): To numerically validate SMPR - an extension of SM incorporating orientation-dependent, susceptibility related relaxation rates and Larmor frequency shifts of the spin echo decay of the dMRI signal.

Approach: To perform Monte-Carlo (MC) simulations in orientationally dispersed, non-exchanging bundles of hollow magnetized cylinders, simulate a standard PGSE signal and its spin echo decay, and compare against the SMPR model.

Results: SMPR is in agreement with the MC simulations in both phase and signal magnitude.

Impact: Orientation-dependent susceptibility effects may improve parameter estimation of the Standard Model of diffusion in white matter and enable rotation-free mapping of susceptibility-related parameters.

Keywords: Microstructure, Susceptibility, Internal Gradient, IGDT, white matter

Motivation: Non-invasively detecting tissue microstructure changes associated with pathologies holds immense diagnostic promise. Our approach complements conventional DWI methods by capturing distinct microstructural information.

Goal(s): We investigate the relationship between axon microstructure parameters and Internal Gradients distributions' influence on MRI signals.

Approach: We employed the Finite Perturber Method (FPM) to calculate intravoxel magnetic gradients and MonteCarlo simulations to simulate the spins diffusion using a MGSE sequence.

Results: Our findings demonstrate up to 2.5 % signal variations when incorporating Internal Gradient Distribution terms, offering insights into signal behavior with varying microstructure parameters.

Impact: The complementary findings of addressing non-invasive tissue-microstructure changes based on probing internal gradients paves the way for early pathological detection. This empowers clinicians to in-depth investigations, transforming our approach to diagnostics and enhancing healthcare outcomes.

Keywords: Simulation/Validation, Simulations

Motivation: There are several generators of white matter phantoms that have been developed in recent years and have significant potential in developing and validating diffusion MRI techniques through computational simulation. However, no such generator has been proposed for grey matter phantoms. 

Goal(s): We aim to provide a means of generating grey matter phantoms compatible with diffusion MRI simulations. 

Approach: Combing the network growth presented in the Contextual Fibre Growth (ConFiG) algorithm with the generative method of topological neuro synthesis (TNS), to create non intersecting morphologically realistic cellular structures. 

Results: We can show that our algorithm can generate non-intersecting realistic voxels of grey matter. 

Impact: We have developed a highly versatile algorithm, ConCeG, which can generate realistic digital phantoms of GM. These phantoms are ready to be incorporated into dMRI simulators, such as Camino, DiSimPy and MCDS for testing and validating diffusion MRI techniques

Keywords: Phantoms, Phantoms, Validation, microstructural Imaging

Motivation: This study utilizes a custom-designed fiber crossing configuration based on anisotropic textile hollow fiber phantom and harnesses high-resolution 14T MRI to unravel manufactured fiber crossings at a microscopic scale. 

Goal(s): By applying structural tensor analysis in combination with eigenvalue decomposition, we have estimated underlying fiber orientations and visualized in multi-planar, directional-color-encoded maps. 

Approach: This innovative approach yielded precise angular measurements across the volume to delineate the expected fiber orientation and crossing angles, thereby validating the structural tensor method's efficacy in capturing complex fiber architecture within a controlled environment. 

Results: This Phantom can provide a ground truth for validating diffusion MRI based crossing assessments.

Impact: This research presents a pivotal advancement for validating MRI-based fiber crossing, offering a novel phantom design for assessing the accuracy and limitations of MRI methods in resolving complex fiber architectures in biological tissues.

Keywords: Simulation/Validation, Phantoms

Motivation: We aim to create a universal procedure for experimental verification of diffusion models using model systems based on glass capillaries.

Goal(s): Spatially ordered glass capillaries mimics the characteristic geometry of white matter and are ideal for performing a 'stress test' of various diffusion models. We aim to verify this through diffusion and relaxation measurements at varying spatial directions and time scales.

Approach: Combined diffusion-  and relaxation-weighted  MRI based measurements verify the geometry of the glass capillaries at different spatial scales. 

Results: The glass capillary phantom was established as a ground truth model for modelling of white matter at different diffusion and relaxation regimes.

Impact: Glass capillaries present a unique object with simple physical and geometrical features mimicking white matter. We tested a glass capillary phantom in terms of a diffusion-relaxation model using conventional sequences and provide a simple theoretical interpretation of experimental results.

Keywords: Microstructure, Microstructure, Spatial Imaging Transcriptomic;Data Integration;Mouse Brain

Motivation: Integration of MRI and spatial transcriptomics may provide a new approach for imaging-genetics research for probing molecular basis underlying observed MRI phenotypes.

Goal(s): We proposed a pipeline for integration of diffusion MRI(dMRI) and spatial transcriptomics of mouse brain to understand potential biological pathway underlying dMRI microstructural models. 

Approach: We spatially co-register population-averaged dMRI maps of mouse brain to 2D spatial transcriptomic for pixelwise correlation and explored genes function and celltype related with dMRI metrics.

Results: We found FA was associated with myelination and oligodendrocyte and water diffusivity was associated with neurons. We identified molecular basis driving two distinct gradients of dMRI in cortex.

Impact: Integration of spatial transcriptomics and MRI enables imaging-genomics analysis at an unprecedented resolution. Our study revealed molecular  basis for typical microstructural markers in diffusion MRI. We further revealed genetic driven force of cortical gradients in axial and radial diffusivity maps.

Keywords: Microstructure, Breast

Motivation: It is imperative to noninvasively assess molecular subtypes in patients with breast cancer, as these play a vital role in guiding treatment approaches and monitoring outcomes. However, conventional apparent diffusion coefficient measurements may not reliably identify the histopathologic differences in molecular subtypes.

Goal(s): We explore the feasibility of time-dependent diffusion MRI (t_d-dMRI) based microstructural mapping for noninvasive identification of molecular subtypes.

Approach: The t_d-dMRI method was validated on breast cancer patients, and microstructural parameters were estimated and compared among molecular subtypes. 

Results: The cellularity and diameter derived from t_d-dMRI proved effective for identifying molecular subtypes in breast cancer.

Impact: Microstructural mapping derived from td-dMRI proves to be an effective method for predicting molecular subtypes, demonstrating unique microstructural properties acroess various molecular subtypes, and thus is promising in personalizing treatment strategies.

Keywords: Microstructure, Microstructure, MR microscopy, Diffusion MR microscopy, Micro-solenoid RF coils, Ultra-high field strength, Mouse spinal cord

Motivation: The research aims to overcome the challenges of performing microscopy to assess the microstructure of mouse spinal cords at high spatial resolution.  

Goal(s): Our goal is to develop a micro-solenoid radiofrequency circuit, which combined with ultra-high field strength and fast imaging sequences, including diffusion MRI, can achieve microscopic-resolution images.

Approach: The study involves the development of a micro-solenoid transceiver coil, and imaging at 15.2 T using fast diffusion imaging sequences to achieve images of excised specimens at microscopic resolution. 

Results: The micro-solenoid radiofrequency circuit significantly improved SNR, enabling high-resolution imaging and accurate data sets for implementing diffusion models at micron-scale resolution.

Impact: High-resolution diffusion imaging may provide estimates of diffusion parameters at a scale more commensurate with the microstructure of the spinal cord than in vivo acquisitions. This will be useful for validating models of water diffusion in complex environments neuronal tissue.

Keywords: Microstructure, Gray Matter, Microstructure, Permeability, fMRI (task based), Multimodal, Small Animals, Preclinical

Motivation: The microstructure-function relationship is essential for our understanding of the healthy and pathological human brain.

Goal(s): In this context, the goal of our study was to investigate microstructural changes arising in the brain during neuronal activity.

Approach: Differences in mean diffusivity MD and mean kurtosis MK time-dependence between rest and active states were assessed in the somatosensory cortex following rat forepaw stimulation.

Results: While no changes were found in control cortical regions, the BOLD-fMRI positive cluster presented a significant decrease in MD and MK during activation. Interestingly, subcortical somatosensory relays displayed the opposite trend which could result from changes in inhibitory/excitatory balance.

Impact: Neuronal activity is accompanied by a myriad of microstructural changes. The diffusion-weighted signal sensitivity to underlying brain microstructure brings new perspectives into the structure-function relationship along with the promise of a functional contrast unbound from the current limitations of BOLD-fMRI.

Keywords: Microstructure, Brain Connectivity, Tensor-Valued Diffusion MRI, Microscopic anisotropy kurtosis (MKA) , Genetic diseases

Motivation: DEAD‐box helicase 24 (DDX24) gene mutations linked to abnormalities of major vessels¹. However, the effect of the gene DDX24 on brain microstructure remains unclear.

Goal(s): Our goal was to demonstrate how advanced tensor-valued diffusion MRI can reveal microstructural alterations in a Ddx24 knockdown mouse model.

Approach: We performed advanced tensor-valued diffusion MRI to examine Ddx24 knockdown mouse brain and evaluated the performance of Ddx24 knockdown mice in the Morris water maze test. 

Results: Ddx24 knockdown mouse revealed declining microscopic anisotropy kurtosis (MK_A) in corpus callosum and hippocampus. Tensor-valued diffusion MRI is a sensitive neuroimaging tool to evaluate gene-edited mouse brain microstructural alterations.

Impact: Advanced tensor-valued diffusion MRI provided cylinders shapes sensitive MK_A and spherical shapes sensitive MKI for detecting microstructural alterations in genetic diseases.

Keywords: Microstructure, White Matter, Standard Modeling, Axon, Myelin, Unmyelinated axons, Diffusion, Validation, Axon loss, Segmentation, Electron microscopy

Motivation: Attaining microstructural specificity to myelinated/unmyelinated axons from macroscopic in vivo quantitative MRI.

Goal(s): Quantifying changes of myelinated and unmyelinated axons using dMRI.

Approach: We apply compartmental diffusion models of white matter, White Matter Tract Integrity (WMTI) and Standard Model Imaging (SMI) to the dMRI signal. We also measure Magnetic Transfer Resonance and R2. We develop automated techniques to segment 2d transmission electron microscopy (TEM) images of white matter into their constituent microstructure and apply volumetric analysis. 

Results: We measured axonal water fraction (AWF) using WMTI and SMI. AWF correlated strongly with our EM volumetric analysis of myelinated and unmyelinated axons.  

Impact: Demyelination and axonal loss occur in neurodegenerative pathologies. This validation study reveals specificity of R2 to myelin volume, while AWF from dMRI detects both unmyelinated and unmyelinated axons. Combining both modalities has the potential to differentiate demyelination from axonal loss.

Keywords: Microstructure, Microstructure, dMRI signal analysis

Motivation: Until now, most attention has been focused on the final dMRI signals acquired, ignoring the signal evolution during diffusion encoding. However, the methods, which incorporated water exchange between compartments into modelling to extract more comprehensive tissue information, need to consider the signal evolution within the different compartments, as described in Karger model.

Goal(s): Figure out the dMRI signal evolution in the simplest case: Gaussian diffusion.

Approach: Theoretical analysis, Monte-Carlo and finite difference simulations.

Results: Signal-evolution curves provided by analytical expressions and numerical simulations are consistent. An “observation-size” effect emerges, the signal-evolution curve depends on the spatial size of the observation area.

Impact: Clarifying the actual dMRI signal evolution during diffusion encoding will inspire us to revisit the theoretical framework of Karger model. The results show that it is necessary to revise the current Karger-model-based methods for the “observation-size” effect.

Keywords: Diffusion Acquisition, Diffusion/other diffusion imaging techniques, exchange, restricted diffusion

Motivation: Thin slices in filter-exchange imaging lead to biased exchange rates, as thin slices require strong crushers. No current approach accounts for crushers in the presence of restricted diffusion.

Goal(s): We set to address the bias in FEXI due to the influence of strong crushers and restricted diffusion.

Approach: Tuned exchange imaging (TEXI) relies on gauging exchange and restriction weighting. We modify FEXI to ensure constant restriction weighting also with strong crushers. The accuracy of exchange mapping was evaluated using Monte Carlo simulations.

Results: TEXI yields consistent exchange rates independent of slice thickness and restriction size even if strong crushers are used.

Impact: TEXI could be useful to maximize exchange sensitivity and specificity with thin slices and in the presence of restricted diffusion.

Keywords: Simulation/Validation, Diffusion/other diffusion imaging techniques

Motivation: Time-dependent diffusion $$$D(t)$$$ is sensitive to brain microstructure. Using Monte Carlo (MC) simulations, $$$D(t)$$$ has been shown to provide information about structural changes caused by pathological conditions.

Goal(s): To establish the relation between the parameters of $$$D(t)$$$ and the extra-axonal space geometry.  

Approach: We solve the Fick-Jacobs equation in the effective medium framework, connect $$$D(t)$$$ to correlations of density and local diffusivity, and validate with Monte-Carlo simulations.

Results: Time-dependence of $$$D(t)$$$ is quantitatively related to geometric characteristics of axonal packing, demyelination and axonal loss. 

Impact: By coarse-graining the extra-axonal space, time-dependent diffusion explores the geometry relevant for demyelination and axonal loss, enabling quantifying axonal microstructure. 

Keywords: Simulation/Validation, Simulations

Motivation: Can two different diffusion experiments (tensor-valued encoding and oscillating-gradient-spin-echo) support the role of axon beading for diffusion restriction in acute ischemic stroke?

Goal(s): To evaluate whether tensor-valued diffusion encoding yields an axon beading model that predicts experimental ischemic changes of diffusivity measured with OGSE.

Approach: Tensor-valued and OGSE/PGSE diffusion MRI were measured in the same acute stroke patients. Monte Carlo simulations were used to assess the links between these two independent measurements.

Results: The tensor-valued derived beading model predictions were in general agreement with independent experiments of less diffusivity reduction with OGSE than PGSE within stroke lesions.

Impact: Novel diffusion MRI sequences such as tensor-valued encoding and oscillating-gradient-spin-echo are complementary methods that point to the same microstructural basis (i.e. beading and elevated intra-cellular volume fraction) for the clinically useful diffusion reduction in acute ischemic stroke.