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Noam Shemesh

Publications and source records attributed to Noam Shemesh.

At least 37 records · Page 2Linked to original sources
arXiv2020

A rapid-onset diffusion functional MRI signal reflects neuromorphological coupling dynamics

Functional Magnetic Resonance Imaging (fMRI) has transformed our understanding of brain function in-vivo. However, the neurovascular coupling mechanisms underlying fMRI are somewhat "distant" from neural activity. Interestingly, evidence from Intrinsic Optical Signals (IOSs) indicates that neural activity is also coupled to (sub)cellular morphological modulations. Diffusion-weighted functional MRI (dfMRI) experiments have been previously proposed to probe such neuromorphological couplings, but the underlying mechanisms have remained highly contested. Here, we provide the first direct link between in vivo ultrafast dfMRI signals upon rat forepaw stimulation and IOSs in acute slices stimulated optogenetically. We reveal a hitherto unreported rapid onset (<100 ms) dfMRI signal which (i) agrees with fast-rising IOSs dynamics; (ii) evidences a punctate quantitative correspondence to the stimulation period; (iii) and is rather insensitive to a vascular challenge. Our findings suggest that neuromorphological coupling can be detected via dfMRI signals, auguring well for future mapping of neural activity more directly.

physics.med-ph↗
arXiv2020

In-vivo Magnetic Resonance Imaging of GABA and Glutamate

Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI) is a molecular imaging methodology capable of mapping brain metabolites with relatively high spatial resolution. Specificity is the main goal of such experiments; yet CEST is confounded by spectral overlap between different molecular species. Here, we overcome this major limitation using a general framework termed overlap-resolved CEST (orCEST) - a kind of spectrally-edited experiment restoring specificity. First, we present evidence revealing that CEST experiments targeting the central nervous system's primary excitatory neurotransmitter, Glutamate (GluCEST) - is significantly contaminated by gamma-aminobutyric acid (GABA) - the primary inhibitory neurotransmitter in the CNS. Then, we harness the novel orCEST methodology to separate Glutamate and - for the first time - GABA signals, thus delivering the desired specificity. In-vivo orCEST experiments resolved the rat brain's primary neurotransmitters and revealed changes in Glutamate and GABA levels upon water deprivation in thirst-related areas. orCEST's features bode well for many applications in neuroscience and biomedicine.

physics.med-ph↗
arXiv2019

Validation and noise robustness assessment of microscopic anisotropy estimation with clinically feasible double diffusion encoding MRI

Purpose: Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical "5-design", could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5-design, and to quantify the proposed method's noise robustness to facilitate future clinical use. Methods: DDE MRI experiments were performed on both ex vivo and in vivo rat brains at 9.4 T using the 5-design and the proposed minimal design and taking into account the difference in the number of acquisitions. The ensuing microscopic fractional anisotropy (μFA) maps were compared over a range of b-values up to 5000 s/mm2. Noise robustness was studied using analytical calculations and numerical simulations. Results: The minimal protocol quantified μFA at an accuracy comparable to the estimates obtained via the more theoretically robust DDE 5-design. μFA's sensitivity to noise was found to strongly depend on compartment anisotropy and tensor magnitude in a non-linear fashion. When μFA < 0.75 or when mean diffusivity is particularly low, very high signal to noise ratio (SNR) is required for precise quantification of μFA. Conclusion: Our work supports using DDE for quantifying microscopic diffusion anisotropy in clinical settings but raises hitherto overlooked precision issues when measuring μFA with DDE and typical clinical SNR.

physics.med-ph↗
arXiv2019

Short echo time relaxation-enhanced MR spectroscopy reveals broad downfield resonances

Purpose: Most MR spectroscopy (MRS) pulse sequences rely on broadband excitation with water saturation and typically focus on upfield signals. By contrast, the downfield spectrum, which contains many potentially useful resonances, is typically not targeted because conventional water-suppressed techniques indirectly saturate the labile protons through exchange. Relaxation-enhanced MRS (RE-MRS) uses frequency-selective excitation while actively avoiding bulk water perturbation, thereby enabling high-quality downfield spectroscopy. However, RE-MRS typically requires very long (typically >40 ms) echo times (TEs) due to its localization module, which inevitably decreases sensitivity and filters shorter T2 components. Here, we overcome this limitation by combining RE-MRS and image selected in vivo spectroscopy (ISIS) localization, abbreviated iRE-MRS, which in turn allows very short TEs (5 ms using our hardware). Methods: Experiments were performed in vitro for validation as well as and in in vivo rat brains at 9.4T. Results: The new iRE-MRS methodology was validated in phantoms where good performance was noted. When the downfield spectrum was investigated at short TEs in in vivo rat brains, iRE-MRS provided very high sensitivity; the ensuing downfield spectra encompassed numerous broad peaks, as well as a broad baseline. All downfield spectral peaks were highly attenuated by increasing TEs as well as by applying water saturation, although to different extent. The signal ratios also varied between TEs, suggesting that exchange rates are different among the downfield signals. Conclusions: Short-TE iRE 1H downfield MRS opens new directions in the investigation of in vivo downfield metabolites and their role on healthy and disease processes.

physics.med-ph↗
arXiv2019

Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis

Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and their animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals of variable disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T2* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results unveil a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. The changes we found in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability. These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and the MS mechanisms.

q-bio.QM↗
arXiv2019

Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry

PURPOSE: Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures. METHODS: PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared. RESULTS: Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of 2 to 4x, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution. CONCLUSION: Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

physics.med-ph↗
arXiv2019

Accurate estimation of microscopic diffusion anisotropy and its time dependence in the mouse brain

Microscopic diffusion anisotropy (μA) has been recently gaining increasing attention for its ability to decouple the average compartment anisotropy from orientation dispersion. Advanced diffusion MRI sequences, such as double diffusion encoding (DDE) and double oscillating diffusion encoding (DODE) have been used for mapping μA. However, the time-dependence of μA has not been investigated insofar, and furthermore, the accuracy of μA estimation vis-à-vis different b-values was not assessed. Here, we investigate both these concepts using theory, simulation, and experiments in the mouse brain. In the first part, simulations and experimental results show that the conventional estimation of microscopic anisotropy from the difference of D(O)DE sequences with parallel and orthogonal gradient directions yields values that highly depend on the choice of b-value. To mitigate this undesirable bias, we propose a multi-shell approach that harnesses a polynomial fit of the signal difference up to third order terms in b-value. In simulations, this approach yields more accurate μA metrics, which are similar to the ground truth values. The second part of this work uses the proposed multi-shell method to estimate the time/frequency dependence of μA. The data shows either an increase or no change in μA with frequency depending on the region of interest, both in white and gray matter. When comparing the experimental results with simulations, it emerges that simple geometric models such as infinite cylinders with either negligible or finite radii cannot replicate the measured trend, and more complex models, which, for example, incorporate structure along the fibre direction are required. Thus, measuring the time dependence of microscopic anisotropy can provide valuable information for characterizing tissue microstructure.

physics.med-ph↗
arXiv2019

Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI

Purpose: Microscopic fractional anisotropy (μFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate μFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for μFA estimation. This study aims to evaluate μFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. Methods: SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The μFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of μFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. Results: μFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). Conclusion: Powder-averaged SMT and (2-component) SM are unable to accurately report μFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.

physics.med-ph↗
arXiv2019

Incomplete Nutation Diffusion Imaging: an ultrafast, single-scan approach for diffusion mapping

Purpose: Diffusion Magnetic Resonance Imaging (dMRI) is confounded by its long acquisition duration, thereby thwarting the detection of rapid microstructural changes, especially when diffusivity variations are accompanied by rapid changes in T2. The purpose of the present study is to accelerate dMRI to a single scan acquisition, and to enable a more accurate estimation of diffusivity as function of time. Methods: A general methodology termed Incomplete Initial Nutation Diffusion Imaging (INDI) capturing two diffusion contrasts in a single shot, is presented. INDI creates a longitudinal magnetization reservoir that facilitates the successive acquisition of two images separated by only a few milliseconds. INDI's theory is presented, followed by proof-of-concept ex- and in-vivo experiments at 16.4 T and 9.4 T. Results: Mean diffusivities (MDs) extracted from INDI were comparable with Diffusion Tensor Imaging (DTI) and the two-shot IDE in the water phantom. As expected in the brain tissues, DTI provided lower MD than UF-IDE and IDE, but IDE and UF-IDE were in excellent agreement. Simulations are presented for identifying the regimes where INDI is most beneficial. Conclusions: INDI accelerates dMRI acquisition to single-shot mode, which can be of great importance for mapping dynamic microstructural properties in-vivo without T2 bias.

physics.med-ph↗
arXiv2019

Two-dimensional Magnetization-Transfer - CPMG MRI reveals tract- specific signatures in fixed rat spinal cord

Multiexponential T2 (MET2) Relaxometry and Magnetization Transfer (MT) are among the most promising MRI-derived techniques for white matter (WM) characterization. Both techniques are shown to have histologically correlated sensitivity to myelin, but these correlations are not fully understood. Furthermore, MET2 and MT reports on different features of WM, thus being specific to different (patho)physiological states. Two-dimensional studies, such as those commonly used in NMR, have been rarely performed in this context. Here, off-resonance irradiation effects on MET2 components were evaluated in fixed rat spinal cord white matter at 16.4T. These 2D MT-MET2 experiments reveal that MT affects both short and long T2 in a tract-specific fashion. The spatially distinct modulations enhanced contrast between microstructurally-distinct spinal cord tracts. Two hypotheses to explain these findings were proposed: either selective elimination of a short T2 component through pre-saturation is combined with intercompartmental water exchange effects that occur on the timescale of irradiation; or other macromolecular species that exist within the tissue - other than myelin - such as neurofilaments, may be involved in the apparent microstructural segregation of the spinal cord (SC) from MET2. Though further investigation is required to elucidate the underlying mechanism, this phenomenon adds a new dimension for WM characterization.

physics.med-ph↗
arXiv2019

Diffusion time dependence of microstructural parameters in fixed spinal cord

Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust as the clinical acquisitions typically probe a regime in which the associated 4th order cumulant expansion is adequate; however, its parameters are not microstructurally specific a priori. Given an appropriate biophysical model, its parameters may be connected to DKI parameters, but it was previously shown that at the DKI level, it still does not provide sufficient information to uniquely determine all model parameters. Earlier work has shown that by neglecting axonal dispersion, this parameter degeneracy reduces to the question of whether intra-axonal diffusivity is larger than or smaller than extra-axonal diffusivity. Here we develop a model of diffusion in spinal cord white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over an unprecedented diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings to predictions from effective medium theory.

physics.med-ph↗
arXiv2019

BOLD-fMRI in the mouse auditory pathway

The auditory pathway is widely distributed throughout the brain, and is perhaps one of the most interesting networks in the context of neuroplasticity. Accurate mapping of neural activity in the entire pathway, preferably noninvasively, and with high resolution, could be instrumental for understanding such longitudinal processes. Functional magnetic resonance imaging (fMRI) has clear advantages for such characterizations, as it is noninvasive, provides relatively high spatial resolution and lends itself for repetitive studies, albeit relying on an indirect neurovascular coupling to deliver its information. Indeed, fMRI has been previously used to characterize the auditory pathway in humans and in rats. In the mouse, however, the auditory pathway has insofar only been mapped using manganese-enhanced MRI. Here, we describe a novel setup specifically designed for high-resolution mapping of the mouse auditory pathway using high-field fMRI. Robust and consistent Blood-Oxygenation-Level-Dependent (BOLD) responses were documented along nearly the entire auditory pathway, from the cochlear nucleus (CN), through the superior olivary complex (SOC), nuclei of the lateral lemniscus (LL), inferior colliculus (IC) and the medial geniculate body (MGB). By contrast, clear BOLD responses were not observed in auditory cortex (AC) in this study. Diverse BOLD latencies were mapped ROI- and pixel-wise using coherence analysis, evidencing different averaged BOLD time courses in different auditory centers. Some degree of tonotopy was identified in the IC, SOC, and MGB in the pooled dataset though it could not be assessed per subject due to a lack of statistical power. Given the importance of the mouse model in plasticity studies, animal models, and optogenetics, and fMRI's potential to map dynamic responses to specific cues, this first fMRI study of the mouse auditory pathway paves the way for future (...)

physics.med-ph↗
arXiv2019

Insights into brain microstructure from in vivo DW-MRS

Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modelling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human (...)

physics.med-ph↗
arXiv2019

Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway

Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD, and its contrasts relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4 T, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, this may entail significant implications towards a much more specific and accurate (...)

physics.med-ph↗
arXiv2019

Axon diameters and myelin content modulate microscopic fractional anisotropy at short diffusion times in fixed rat spinal cord

Mapping tissue microstructure accurately and noninvasively is one of the frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is at the forefront of such efforts, as it is capable of reporting on microscopic structures orders of magnitude smaller than the voxel size by probing restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating Diffusion Encoding (DODE) in particular, are highly promising for their ability to report on microscopic fractional anisotropy (μFA), a measure of the pore anisotropy in its own eigenframe, irrespective of orientation distribution. However, the underlying correlates of μFA have insofar not been studied. Here, we extract μFA from DDE and DODE measurements at ultrahigh magnetic field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We further endeavor to correlate μFA with Myelin Water Fraction (MWF) derived from multiexponential T2 relaxometry, as well as with literature-based spatially varying axonal diameters. In addition, a simple new method is presented for extracting unbiased μFA from three measurements at different b-values. Our findings reveal strong anticorrelations between μFA (derived from DODE) and axon diameter in the distinct spinal cord tracts; a moderate correlation was also observed between μFA derived from DODE and MWF. These findings suggest that axonal membranes strongly modulate μFA, which - owing to its robustness towards orientation dispersion effects - reflects axon diameter much better than its typical FA counterpart. The μFA exhibited modulations when measured via oscillating or blocked gradients, suggesting selective probing of different parallel path lengths and providing insight into how those modulate μFA metrics. Our findings thus shed light into the underlying microstructural correlates of μFA and are (...)

physics.med-ph↗
arXiv2019

Effects of nongaussian diffusion on "isotropic diffusion measurements'': an ex-vivo microimaging and simulation study

Designing novel diffusion-weighted pulse sequences to probe tissue microstructure beyond the conventional Stejskal-Tanner family is currently of broad interest. One such technique, multidimensional diffusion MRI, has been recently proposed to afford model-free decomposition of diffusion signal kurtosis into terms originating from either ensemble variance of isotropic diffusivity or microscopic diffusion anisotropy. This ability rests on the assumption that diffusion can be described as a sum of multiple Gaussian compartments, but this is often not strictly fulfilled. The effects of nongaussian diffusion on single shot isotropic diffusion sequences were first considered in detail by de Swiet and Mitra in 1996. They showed theoretically that anisotropic compartments lead to anisotropic time dependence of the diffusion tensors, which causes the measured isotropic diffusivity to depend on gradient frame orientation. Here we show how such deviations from the multiple Gaussian compartments assumption conflates orientation dispersion with ensemble variance in isotropic diffusivity. Second, we consider additional contributions to the apparent variance in isotropic diffusivity arising due to intracompartmental kurtosis. These will likewise depend on gradient frame orientation. We illustrate the potential importance of these confounds with analytical expressions, numerical simulations in simple model geometries, and microimaging experiments in fixed spinal cord using isotropic diffusion encoding waveforms with 7.5 ms duration and 3000 mT/m maximum amplitude.

physics.bio-ph↗
arXiv2017

White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging

White matter tract integrity (WMTI) can characterize brain microstructure in areas with highly aligned fiber bundles. Several WMTI biomarkers have now been validated against microscopy and provided promising results in studies of brain development and aging, and in a number of brain disorders. Currently, WMTI is mostly used in dedicated animal studies and clinical studies of slowly progressing diseases but has not yet emerged as a routine clinical tool. To this end, a less data intensive experimental method would be beneficial by enabling high resolution validation studies, and ease clinical applications by speeding up data acquisition compared to typical diffusion kurtosis imaging (DKI) protocols utilized as part of WMTI imaging. Here, we evaluate WMTI based on recently introduced axially symmetric DKI which has lower data demand than conventional DKI. We compare WMTI parameters derived from conventional DKI to those calculated analytically from axially symmetric DKI. We employ numerical simulations, as well as data from fixed rat spinal cord (1) and in vivo human (3) and rat brain (4). Our analysis shows that analytical WMTI based on axially symmetric DKI with sparse data sets (19 images) produces WMTI metrics that correlate strongly with estimates based on traditional DKI data sets (60 images or more). We demonstrate the preclinical potential of the proposed WMTI technique in in vivo rat brain (300 μm isotropic resolution with whole brain coverage). WMTI parameter estimates are subject to a duality leading to two solution branches dependent on a sign choice which is currently debated. Results from both of these branches are presented and discussed throughout our analysis. The proposed fast WMTI approach may be useful for preclinical research and e.g. clinical evaluation of patients with traumatic white matter injuries or symptoms of neurovascular or neuroinflammatory disorders.

physics.med-ph↗