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Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre‐urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion‐weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion‐weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non‐Gaussian diffusion, and hybrid IVIM‐DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. Level of Evidence: 5 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Introduction: Magnetic resonance imaging (MRI) enables the investigation of pathological changes in gray and white matter at the lumbosacral enlargement (LSE) and conus medullaris (CM). However, conducting group-level analyses of MRI metrics in the lumbosacral spinal cord is challenging due to variability in CM length, lack of established image-based landmarks, and unknown scan-rescan reliability. This study aimed to improve inter-subject alignment of the lumbosacral cord to facilitate group-level analyses of MRI metrics. Additionally, we evaluated the scan-rescan reliability of MRI-based cross-sectional area (CSA) measurements and diffusion tensor imaging (DTI) metrics. Methods: Fifteen participants (10 healthy volunteers and 5 patients with spinal cord injury) underwent axial T2*-weighted and diffusion MRI at 3T. We assessed the reliability of spinal cord and gray matter-based landmarks for inter-subject alignment of the lumbosacral cord, the inter-subject variability of MRI metrics before and after adjusting for the CM length, the intra- and inter-rater reliability of CSA measurements, and the scan-rescan reliability of CSA measurements and DTI metrics. Results: The slice with the largest gray matter CSA as an LSE landmark exhibited the highest reliability, both within and across raters. Adjusting for the CM length greatly reduced the inter-subject variability of MRI metrics. The intra-rater, inter-rater, and scan-rescan reliability of MRI metrics were the highest at and around the LSE (scan-rescan coefficient of variation <3% for CSA measurements and <7% for DTI metrics within the white matter) and decreased considerably caudal to it. Conclusions: To facilitate group-level analyses, we recommend using the slice with the largest gray matter CSA as a reliable LSE landmark, along with an adjustment for the CM length. We also stress the significance of the anatomical location within the lumbosacral cord in relation to the reliability of MRI metrics. The scan-rescan reliability values serve as valuable guides for power and sample size calculations in future longitudinal studies. [ABSTRACT FROM AUTHOR]

Purpose: Preliminary study to determine whether double pulsed field gradient (PFG) diffusion MRI is sensitive to key features of muscle microstructure related to function. Methods: The restricted diffusion profile of molecules in models of muscle microstructure derived from histology were systematically simulated using a numerical simulation approach. Diffusion tensor subspace imaging analysis of the diffusion signal was performed, and spherical anisotropy (SA) was calculated for each model. Linear regression was used to determine the predictive capacity of SA on the fiber area, fiber diameter, and surface area to volume ratio of the models. Additionally, a rat model of muscle hypertrophy was scanned using a single PFG and a double PFG pulse sequence, and the restricted diffusion measurements were compared with histological measurements of microstructure. Results: Excellent agreement between SA and muscle fiber area (r2 = 0.71; p < 0.0001), fiber diameter (r2 = 0.83; p < 0.0001), and surface area to volume ratio (r2 = 0.97; p < 0.0001) in simulated models was found. In a scanned rat leg, the distribution of these microstructural features measured from histology was broad and demonstrated that there is a wide variance in the microstructural features observed, similar to the SA distributions. However, the distribution of fractional anisotropy measurements in the same tissue was narrow. Conclusions: This study demonstrates that SA—a scalar value from diffusion tensor subspace imaging analysis—is highly sensitive to muscle microstructural features predictive of function. Furthermore, these techniques and analysis tools can be translated to real experiments in skeletal muscle. The increased dynamic range of SA compared with fractional anisotropy in the same tissue suggests increased sensitivity to detecting changes in tissue microstructure. [ABSTRACT FROM AUTHOR]

Background: Renal diffusion‐weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity. Purpose: To measure modulation of DWI with cardiac phase and flow‐compensated (FC) diffusion gradient waveforms. Study Type: Prospective. Population: Six healthy volunteers (ages: 22–48 years, five females), water phantom. Field Strength/Sequence: 3‐T, prototype DWI sequence with 2D echo‐planar imaging, and bipolar (BP) or FC gradients. 2D Half‐Fourier Single‐shot Turbo‐spin‐Echo (HASTE). Multiple‐phase 2D spoiled gradient‐echo phase contrast (PC) MRI. Assessment: BP and FC water signal decays were qualitatively compared. Renal arteries and velocities were visualized on PC‐MRI. Systolic (peak velocity), diastolic (end stable velocity), and pre‐systolic (before peak velocity) phases were identified. Following mutual information‐based retrospective self‐registration of DWI within each kidney, and Marchenko‐Pastur Principal Component Analysis (MPPCA) denoising, combined IVIM‐DTI analysis estimated mean diffusivity (MD), fractional anisotropy (FA), and eigenvalues (λi) from tissue diffusivity (Dt), perfusion fraction (fp), and pseudodiffusivity (Dp, Dp,axial, Dp,radial), for each tissue (cortex/medulla, segmented on b0/FA respectively), phase, and waveform (BP, FC). Monte Carlo water diffusion simulations aided data interpretation. Statistical Tests: Mixed model regression probed differences between tissue types and pulse sequences. Univariate general linear model analysis probed variations among cardiac phases. Spearman correlations were measured between diffusion metrics and renal artery velocities. Statistical significance level was set at P < 0.05. Results: Water BP and FC signal decays showed no differences. Significant pulse sequence dependence occurred for λ1, λ3, FA, Dp, fp, Dp,axial, Dp,radial in cortex and medulla, and medullary λ2. Significant cortex/medulla differences occurred with BP for all metrics except MD (systole [P = 0.224]; diastole [P = 0.556]). Significant phase dependence occurred for Dp, Dp,axial, Dp,radial for BP and medullary λ1, λ2, λ3, MD for FC. FA correlated significantly with velocity. Monte Carlo simulations indicated medullary measurements were consistent with a 34 μm tubule diameter. Data Conclusion: Cardiac gating and flow compensation modulate of measurements of renal diffusion. Evidence Level: 2 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Background: Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application. Purpose: To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics. Study Type: Prospective. Subjects: Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15). Field Strength/Sequence: Two‐dimensional diffusion MRI (b = 1000 s/mm2) at a field strength of 3 T. Sessions in healthy subjects—standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients—two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes). Assessment: Fifteen direction DTI was repeated twice for within‐session test–retest measurements in healthy subjects. Bland–Altman analysis computed bias and limits of agreement for DTI metrics using test–retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes). Statistical Tests: Bland–Altman and ICC analysis using a P‐value of 0.05 for statistical significance. Results: Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter. Conclusion: A nested cubes diffusion gradient table produces tensor‐based and dynamic diffusion measurements in a single acquisition. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

This study compares remote neurodegenerative changes caudal to a cervical injury in degenerative cervical myelopathy (DCM; i.e., non-traumatic) and incomplete traumatic spinal cord injury (tSCI) patients, using magnetic resonance imaging (MRI)–based tissue area measurements and diffusion tensor imaging (DTI). Eighteen mild-to-moderate DCM patients with sensory impairments (modified Japanese Orthopedic score: 16.2 ± 1.9), 14 incomplete tetraplegic tSCI patients (American Spinal Injury Association Impairment Scale C and D), and 20 healthy controls were recruited. All participants received DTI and T2*-weighted scans in the lumbosacral enlargement (caudal to injury) and at C2/C3 (rostral to injury). MRI readouts included DTI metrics in the white matter (WM) columns and cross-sectional WM and gray matter area. One-way analysis of variance with Tukey's post hoc comparison (p < 0.05) was used to assess group differences. In the lumbosacral enlargement, compared with DCM, tSCI patients exhibited decreased fractional anisotropy in the lateral (tSCI vs. DCM, -11.9%, p = 0.007) and ventral WM column (-8.0%, p = 0.021), and showed a trend toward lower values in the dorsal column (-8.9%, p = 0.068). At C2/C3, compared with controls, fractional anisotropy was lower in both groups in the dorsal (DCM vs. controls, -7.9%, p = 0.024; tSCI vs. controls, -10.0%, p = 0.007) and in the lateral column (DCM: -6.2%, p = 0.039; tSCI: -13.3%, p < 0.001), while tSCI patients had lower fractional anisotropy than DCM patients in the lateral column (-7.6%, p = 0.029). WM areas were not different between patient groups but were lower compared with controls in the lumbosacral enlargement (DCM: -16.9%, p < 0.001; tSCI: -10.5%, p = 0.043) and at C2/C3 (DCM: -16.0%, p < 0.001; tSCI: -18.1%, p < 0.001). In conclusion, mild-to-moderate DCM and incomplete tSCI lead to similar degree of degeneration of the dorsal and lateral columns at C2/C3, but tSCI results in more widespread white matter damage in the lumbosacral enlargement. These remote changes are likely to contribute to the patients' impairment and recovery. DTI is a sensitive tool to assess remote pathological changes in DCM and tSCI patients. [ABSTRACT FROM AUTHOR]

Neural tissue is a hierarchical multiscale system with intracellular and extracellular diffusion compartments at different length scales. The normal diffusion of bulk water in tissues is not able to detect the specific features of a complex system, providing nonlocal, diffusion measurement averaged on a 10-20 μm length scale. Being able to probe tissues with sub-micrometric diffusion length and quantify new local parameters, transient anomalous diffusion (tAD) would dramatically increase the diagnostic potential of diffusion MRI (DMRI) in detecting collective and sub-micro architectural changes of human tissues due to pathological damage. In DMRI, the use of tAD parameters quantified using specific DMRI acquisition protocols and their interpretation has often aroused skepticism. Although the derived formulas may accurately fit experimental diffusion-weighted data, the relationships between the postulated dynamical feature and the underlying geometrical structure remains elusive, or at most only suggestive. This work aimed to elucidate and validate the image contrast and information that can be obtained using the tAD model in white matter (WM) through a direct comparison between different diffusion metrics and histology. Towards this goal, we compared tAD metrics extracted from pure subdiffusion (α-imaging) and super-pseudodiffusion (γ-imaging) in excised mouse spinal cord WM, together with T2 and T2* relaxometry, conventional (normal diffusion-based) diffusion tensor imaging (DTI) and q-space imaging (QSI), with morphologic measures obtained by optical microscopy, to determine which structural and topological characteristics of myelinated axons influenced tAD contrast. Axon diameter (AxDiam), the standard deviation of diameters (SD ax.diam ), axonal density (AxDens) and effective local density (ELD) were extracted from optical images in several WM tracts. Among all the diffusion parameters obtained at 9.4 T, γ-metrics confirmed a strong dependence on magnetic in-homogeneities quantified by R2* = 1/T2* and showed the strongest associations with AxDiam and ELD. On the other hand, α-metrics showed strong associations with SD ax.diam and was significantly related to AxDens, suggesting its ability to quantify local heterogeneity degree in neural tissue. These results elucidate the biophysical mechanism underpinning tAD parameters and show the clinical potential of tAD-imaging, considering that both physiologic and pathologic neurodegeneration translate into alterations of WM morphometry and topology. [ABSTRACT FROM AUTHOR]

Purpose: To use diffusion measurements to map the spatial dependence of the magnetic field produced by the gradient coils of an MRI scanner with sufficient accuracy to correct errors in quantitative diffusion MRI (DMRI) caused by gradient nonlinearity and gradient amplifier miscalibration. Theory and Methods: The field produced by the gradient coils is expanded in regular solid harmonics. The expansion coefficients are found by fitting a model to a minimum set of diffusion‐weighted images of an isotropic diffusion phantom. The accuracy of the resulting gradient coil field maps is evaluated by using them to compute corrected b‐matrices that are then used to process a multi‐shell diffusion tensor imaging (DTI) dataset with 32 diffusion directions per shell. Results: The method substantially reduces both the spatial inhomogeneity of the computed mean diffusivities (MD) and the computed values of the fractional anisotropy (FA), as well as virtually eliminating any artifactual directional bias in the tensor field secondary to gradient nonlinearity. When a small scaling miscalibration was purposely introduced in the x, y, and z, the method accurately detected the amount of miscalibration on each gradient axis. Conclusion: The method presented detects and corrects the effects of gradient nonlinearity and gradient gain miscalibration using a simple isotropic diffusion phantom. The correction would improve the accuracy of DMRI measurements in the brain and other organs for both DTI and higher order diffusion analysis. In particular, it would allow calibration of MRI systems, improving data harmony in multicenter studies. [ABSTRACT FROM AUTHOR]

Although it is known that health is not merely the absence of disease, the positive aspects of mental health have been less comprehensively researched compared with its negative aspects. Subjective well-being (SWB) is one of the indicators of positive psychology, and high SWB is considered to benefit individuals in multiple ways. However, the neural mechanisms underlying individual differences in SWB remain unclear, particularly in terms of brain microstructural properties as detected by diffusion tensor imaging. The present study aimed to investigate the relationship between measurements of diffusion tensor imaging [mean diffusivity (MD) and fractional anisotropy] and the degree of SWB as measured using a questionnaire. Voxel-based analysis was used to investigate the association between MD and SWB scores in healthy young adults (age, 20.7 ± 1.8 years; 695 males and 514 females). Higher levels of SWB were found to be associated with lower MD in areas surrounding the right putamen, insula, globus pallidus, thalamus and caudate. These results indicated that individual SWB is associated with variability in brain microstructural properties. [ABSTRACT FROM AUTHOR]

Mild TBI, characterized by microstructural damage, often undetectable on conventional imaging techniques, is a pervasive condition that disturbs brain function and can potentially result in long-term deficits. Deciphering the underlying microstructural damage in mild TBI is crucial for establishing a reliable diagnosis and enabling effective therapeutics. Efforts to capture this damage have been extensive, but results have been inconsistent and incomplete. To that effect, we set out to examine the shape of the diffusion tensor in mild TBI during the acute phase of injury. We inspected diffusivity and geometric measurements describing the diffusion tensor's shape and compared mild TBI (N = 34, 20.4–66.6 yo) measurements with those from healthy control (N = 42, 20.7–67.2 yo) participants using voxelwise tract-based spatial statistics. Subsequently, to explore associations between the diffusion measurements in mild TBI, we performed nonparametric statistics and machine learning techniques. Overall, mild TBI displayed a diffuse increase in D λ2 , D λ3 , D radial , D mean , and C spherical , with a diffuse decrease in A fractional , A mode , and C linear , in addition to no change in D axial or C planar. Most notably, our results provide evidence for D radial as a potential biomarker for microstructural damage, specifically its main component D λ2 , based on their performance in discriminating between mild TBI and control groups. A fractional was also found to be important for discriminating between groups. Our results revealed the importance of a diffusion measurement often overlooked, D radial , in assessing TBI and suggest differentiating diffusion measurements has the potential utility to detect variations in the underlying pathophysiology after injury. • Diffuse disruption in diffusivity and geometric diffusion measurements in mild TBI. • Mild TBI displayed increased radial diffusivity and decreased fractional anisotropy. • Fractional anisotropy was the most disrupted diffusion measurement via TBSS. • Radial diffusivity indicative of microstructural damage following acute, mild TBI. [ABSTRACT FROM AUTHOR]

The dependence of the diffusion tensor on frequency is of great interest in studies of tissue microstructure because it reveals restrictions to the Brownian motion of water molecules caused by cell membranes. Oscillating gradient spin‐echo (OGSE) sequences can sample this dependence with gradient shapes for which the power spectrum of the zeroth moment is focused at a target frequency. In order to maintain the total spectral power (ie the b‐value), oscillating gradient amplitudes must grow with the frequency squared. For this reason, OGSE applications on clinical MRI scanners are limited to low frequencies, for which it is difficult to obtain a narrow frequency bandwidth of the gradient moment in a useful echo time. In particular, the commonly used pair of single‐period trapezoidal‐cosine pulses separated by a half‐period produces significant side lobes away from the target frequency. To mitigate this effect, improved OGSE waveforms are proposed, which reduce the gap between the two gradient pulses to the minimum duration required for the refocusing RF pulse. Additionally, a slight deviation from the periodicity of the waveforms is proposed in order to permit using the maximum slew rate of the gradient system for all lobes of trapezoidal waveforms while maintaining advantageous spectral properties, which is not the case for the currently used OGSE sequences. Numerical calculations validate these changes, showing that both modifications significantly narrow the gradient moment power spectrum and increase the contribution of its main lobe to the b‐value, thus improving the specificity of the measurement. The utility of the new shapes is demonstrated by diffusion tensor measurements of human white matter in vivo over the range of 30‐75 Hz with a b‐value of nearly 1000 s/mm2, using a high‐performance gradient insert. However, the improvement should increase the sampling precision of OGSE experiments for all gradient systems. [ABSTRACT FROM AUTHOR]

Purpose : In Type 2 diabetes (T2DM), white matter (WM) pathology has been suggested to play an important role in the etiology of T2DM-related cognitive impairment. This study aims to investigate the integrity of the cingulum bundle (CB), a major WM tract, in T2DM patients using diffusion tensor tractography. Methods : Thirty-seven T2DM patients and 34 age-, sex- and education matched healthy controls were included and underwent diffusion tensor imaging. Tractography of bilateral CB tracts was performed and diffusion measurements were compared between the two groups. Next, brain regions with significant group differences on fractional anisotropy (FA) values were set as the region of interest (ROI), and the CB fibers that passed through were identified. Diffusion measures were extracted from these fibers to investigate their correlations with the cognitive performances and endocrine parameters. Results : T2DM patients exhibited decreased FA in bilateral CB, increased mean diffusion (MD) in the right CB, and decreased length in the left CB. Through voxel-wise comparison, the most prominent FA difference was identified in the posterior segment of the CB and the reconstructed tract was part of the retrosplenial component. Importantly, the diffusion measurements of the tract were significantly correlated with the impaired performance in executive functioning and elevated insulin resistance (IR) in the T2DM group, instead of the control group. Conclusions : The diffusion measurements in bilateral CB were altered in T2DM patients, which might reflect important neuropathologic changes in the fibers. Our study adds to knowledge about how the cingulum changes structurally along its entire length in T2DM and highlights the relationship between WM and cognitive performance. Besides, IR might be an important risk factor that warrants further exploration. [ABSTRACT FROM AUTHOR]

Objectives: To assess the ability of quantitative T2, diffusion tensor imaging (DTI) and radiologist's scores to detect muscle changes following acute muscle tear in soccer and rugby players. To assess the ability of these parameters to predict return to play times. Methods: In this prospective, longitudinal study, 13 male athletes (age 19 to 34 years; mean 25 years) underwent MRI within 1 week of suffering acute muscle tear. Imaging included measurements of T2 and DTI parameters. Images were also assessed using modified Peetrons and British athletics muscle injury classification (BAMIC) scores. Participants returned for a second scan within 1 week of being determined fit to return to play. MRI measurements were compared between visits. Pearson's correlation between visit 1 measurements and return to play times was assessed. Results: There were significant differences between visits in BAMIC scores (Z = − 2.088; p = 0.037), modified Peetrons (Z = − 2.530; p = 0.011) and quantitative MRI measurements; T2, 13.12 ms (95% CI, 4.82 ms, 21.42 ms; p = 0.01); mean diffusivity (0.22 (0.04, 0.39); p = 0.02) and fractional anisotropy (0.07 (0.01, 0.14); p = 0.03). BAMIC scores showed a significant correlation with return to play time (Rs = 0.64; p = 0.02), but modified Peetrons scores and quantitative parameters did not. Conclusions: T2 and DTI measurements in muscle can detect changes due to healing following muscle tear. Although BAMIC scores correlated well with return to play times, in this small study, quantitative MRI values did not, suggesting that T2 and DTI measurements are inferior predictors of return to play time compared with visual scoring. Key Points: • Muscle changes following acute muscle tear can be measured using T2 and diffusion measurements on MRI. • Measurements of T2 and diffusion using MRI are not as good as a radiologist's visual report at predicting return to play time after acute muscle tear. [ABSTRACT FROM AUTHOR]

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1‐4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b‐value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal‐to‐noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra‐ and extra‐hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub‐fields. The perforant path, subiculum and angular bundle demonstrated extra‐hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity. [ABSTRACT FROM AUTHOR]

Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration; however, their histopathological basis remains unclear.Methods: In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food.Results: Using specific immune-histological markers, we demonstrated that 2 weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus, and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior.Conclusion: Network-specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion-based imaging of microstructural integrity and network characteristics. [ABSTRACT FROM AUTHOR]

Objectives: White matter (WM) impairments involving both motor and extra-motor areas have been well-documented in amyotrophic lateral sclerosis (ALS). This study tested the potential of diffusion measurements in WM for identifying ALS based on support vector machine (SVM). Methods: Voxel-wise fractional anisotropy (FA) values of diffusion tensor images (DTI) were extracted from 22 ALS patients and 26 healthy controls and served as discrimination features. The revised ALS Functional Rating Scale (ALSFRS-R) was employed to assess ALS severity. Feature ranking and selection were based on Fisher scores. A linear kernel SVM algorithm was applied to build the classification model, from which the classification performance was evaluated. To promote classifier generalization ability, a leave-one-out cross-validation (LOOCV) method was adopted. Results: By using the 2,400~3,400 ranked features as optimal features, the highest classification accuracy of 83.33% (sensitivity = 77.27% and specificity = 88.46%, P = 0.0001) was achieved, with an area under receiver operating characteristic curve of 0.862. The predicted function value was positively correlated with patient ALSFRS-R scores (r = 0.493, P = 0.020). In the optimized SVM model, FA values from several regions mostly contributed to classification, primarily involving the corticospinal tract pathway, postcentral gyrus, and frontal and parietal areas. Conclusions: Our results suggest the feasibility of ALS diagnosis based on SVM analysis and diffusion measurements of WM. Additional investigations using a larger cohort is recommended in order to validate the results of this study. [ABSTRACT FROM AUTHOR]

Background: Quantitative diffusion MRI is a promising technique for evaluating peripheral nerve integrity but low signal-to-noise ratio (SNR) can impede measurement accuracy.Purpose: To evaluate principal component analysis (PCA) and generalized spherical deconvolution (genSD) denoising techniques to improve within-subject reproducibility and peripheral nerve conspicuity.Study Type: Prospective.Subjects: Seven healthy volunteers and three peripheral neuropathy patients.Field Strength/sequence: 3T/multiband single-shot echo planar diffusion sequence using multishell 55-direction scheme.Assessment: Images were processed using four methods: "original" (no denoising), "average" (10 repetitions), "PCA-only," and "PCA + genSD." Tibial and common peroneal nerve segmentations and masks were generated from volunteer diffusion data. Quantitative (SNR and contrast-to-noise ratio [CNR]) values were calculated. Three radiologists qualitatively evaluated nerve conspicuity for each method. The two denoising methods were also performed in three patients with peripheral neuropathies.Statistical Tests: For healthy volunteers, calculations included SNR and CNRFA (computed using FA values). Coefficient of variation (CV%) of CNRFA quantified within-subject reproducibility. Groups were compared with two-sample t-tests (significance P < 0.05; two-tailed, Bonferroni-corrected). Odds ratios (ORs) quantified the relative rates of each of three radiologists confidently identifying a nerve, per slice, for the four methods.Results: "PCA + genSD" yielded the highest SNR (meanoverall = 14.83 ± 1.99) and tibial and common peroneal nerve CNRFA (meantibial = 3.45, meanperoneal = 2.34) compared to "original" (P SNR < 0.001; P CNR = 0.011) and "PCA-only" (P SNR < 0.001, P CNR < 0.001). "PCA + genSD" had higher within-subject reproducibility (low CV%) for tibial (6.04 ± 1.98) and common peroneal nerves (8.27 ± 2.75) compared to "original" and "PCA-only." The mean FA was higher for "original" than "average" (P < 0.001), but did not differ significantly between "average" and "PCA + genSD" (P = 0.14). "PCA + genSD" had higher tibial and common peroneal nerve conspicuity than "PCA-only" (ORtibial = 2.50, P < 0.001; ORperoneal = 1.86, P < 0.001) and "original" (ORtibial = 2.73, P < 0.001; ORperoneal = 2.43, P < 0.001).Data Conclusion: PCA + genSD denoising method improved SNR, CNRFA , and within-subject reproducibility (CV%) without biasing FA and nerve conspicuity. This technique holds promise for facilitating more reliable, unbiased diffusion measurements of peripheral nerves.Level Of Evidence: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1128-1137. [ABSTRACT FROM AUTHOR]

In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10−3 mm2/s, Δ2b: −11.2%, Δmono: −14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10−3 mm2/s, Δ2b: −9.4%, Δmono: −11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10−3 mm2/s, Δ2b: −5.5%, Δmono: −11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion. [ABSTRACT FROM AUTHOR]

Objectives: The goal of this study is to examine the effect of contrast agent (CA) dose and diffusion coefficient on the estimation of vessel size index (VSI). Materials and methods: Three groups of four participants were enrolled in this study and two different experiments were performed. Different dose of CA, namely 0.1 mmol/kg and 0.05 mmol/kg were assessed in two groups of normal subjects. Diffusion coefficient effect was assessed in the third group with high-grade glioma. Imaging included gradient echo and spin-echo DSC and DTI on a 3-T MR Scanner. Results: VSI estimation using half of standard dose of CA showed higher values compared to the application of standard, with a ratio of 2 for the WM and 1.5 for the GM. VSI estimates for tumor tissues (22 µm) were considerably higher compared to contra-lateral Normal-Appearing WM (NAWM, 4 µm, P < 0.01) and Normal-Appearing GM (NAGM, 8 µm, P < 0.04). Discussion: Application of standard dose for CA injection and also taking into account the effect of diffusion coefficient can lead to a better correlation of VSI with previous theoretically predicted values and improvement of individual diagnostics in tumor evaluations. [ABSTRACT FROM AUTHOR]

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture. [ABSTRACT FROM AUTHOR]

• Propose cardiac diffusion tensor imaging (DTI) simulation using Bloch equation and Monte Carlo random walking method based on realistic myocardium model reconstructed from polarized light imaging (PLI) data of whole human hearts. • Match cardiac DTI simulation and real DTI acquisitions of the same hearts. • Investigate relationships between microscopic myocardial structures and macroscopic DTI measurements. [Display omitted] To investigate the relationship between microscopic myocardial structures and macroscopic measurements of diffusion tensor imaging (DTI), we proposed a cardiac DTI simulation method using the Bloch equation and the Monte Carlo random walk in a realistic myocardium model reconstructed from polarized light imaging (PLI) data of the entire human heart. To obtain a realistic simulation, with the constraints of prior knowledge pertaining to the maturational change of the myocardium structure, appropriate microstructure modeling parameters were iteratively determined by matching DTI simulations and real acquisitions of the same hearts in terms of helix angle, fractional anisotropy (FA) and mean diffusivity (MD) maps. Once a realistic simulation was obtained, we varied the extra-cellular volume (ECV) ratio, myocyte orientation heterogeneity and myocyte size, and explored the effects of microscopic changes in tissue structure on macroscopic diffusion metrics. The experimental results demonstrated the feasibility of simulating the DTI of the whole heart using PLI measurements. When varying ECV from 15% to 55%, mean FA decreased from 0.55 to 0.26, axial diffusivity increased by 0.6 μ m 2 / m s , and radial diffusivity increased by 0.7 μ m 2 / m s. When orientation heterogeneity was varied from 0 to 20 ∘ , mean FA decreased from 0.4 to 0.3, axial diffusivity decreased by 0.08 μ m 2 / m s , and radial diffusivity increased by 0.03 μ m 2 / m s. When mean diameter of myocytes was varied from 6 μ m to 10 μ m , FA decreased from 0.67 to 0.46, axial and radial diffusivities increased by 0.05 and 0.2 μ m 2 / m s , respectively. [ABSTRACT FROM AUTHOR]

We measure all nonzero elements of the three-dimensional diffusion tensor D for clusters of colloidal spheres to a precision of 1% or better using digital holographic microscopy. We study both dimers and triangular trimers of spheres, for which no analytical calculations of the diffusion tensor exist. We observe anisotropic rotational and translational diffusion arising from the asymmetries of the clusters. In the case of the three-particle triangular cluster, we also detect a small but statistically significant difference in the rotational diffusion about the two in-plane axes. We attribute this difference to weak breaking of threefold rotational symmetry due to a small amount of particle polydispersity. Our experimental measurements agree well with numerical calculations and show how diffusion constants can be measured under conditions relevant to colloidal self-assembly, where theoretical and even numerical prediction is difficult. [ABSTRACT FROM AUTHOR]

• We propose to use machine learning to estimate fiber orientation distribution (fODF). • Our model maps the resampled diffusion signal to fODF using a multilayer perceptron. • We develop methods for synthesizing reliable training data in-silico. • We show that the proposed method has important merits compared with existing methods. Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a powerful MRI modality that allows the investigation of the microstructure of tissues both in vivo and noninvasively. Its reliability is strictly dependent on the performance of diffusion‐sensitizing gradients, of which spatial nonuniformity is a known issue in the case of virtually all clinical MRI scanners. The influence of diffusion gradient inhomogeneity on the accuracy of the diffusion tensor imaging was investigated by means of computer simulations supported by an MRI experiment performed at the isocenter and 15 cm away. The DTI measurements of two diffusion phantoms were simulated assuming a nonuniform diffusion‐sensitizing gradient and various levels of noise. Thereafter, the tensors were calculated by two methods: (i) assuming a spatially constant b‐matrix (standard DTI) and (ii) applying the b‐matrix spatial distribution in the DTI (BSD‐DTI) technique, a method of indicating the b‐matrix for each voxel separately using an anisotropic phantom as a standard of diffusion. The average eigenvalues and fractional anisotropy across the homogeneous region of interest were calculated and compared with the expected values. Diffusion gradient inhomogeneity leads to overestimation of the largest eigenvalue, underestimation of the smallest one and thus overestimation of fractional anisotropy. The effect is similar to that caused by noise; however, it could not be corrected by increasing SNR. The MRI measurements, performed using a 3 T clinical scanner, revealed that the split of the eigenvalues measured 15 cm away from the isocenter is significant (up to 25%). The BSD‐DTI calibration allowed the reduction of the measured fractional anisotropy of the isotropic medium from 0.174 to 0.031, suggesting that gradient inhomogeneity was the main cause of this error. For the phantom measured at the isocenter, however, the split was almost not observed; the average eigenvalues were shifted from the expected value by ~ 5%. [ABSTRACT FROM AUTHOR]