Background:Diffusion tensor imaging (DTI) has proven valuable in assessing structural and architectural features of white matter (WM) in postnatal development. Diffusion tensor-based morphometry (DTBM) uses DTI data to measure local volume changes and has been demonstrated in previous studies to be informative in the evaluation of specific WM pathways in several neurological disorders. In this study, we assess DTBM volume changes during postnatal brain development in typically developing children. In addition, we evaluate in each pathway the relationship between changes in volume and DTI metrics. Method:We included DTI data from 182 healthy participants in the age range of 0-21 years, from the publicly available database: the NIH Pediatric MRI Data (NIHPD). Data were processed using the TORTOISE pipeline and age-specific templates were created using the diffusion tensor-based registration tool DRTAMAS. Region of interests (ROIs) were defined on a study-specific, young-adult reference template (18-21 years). Individual brains were registered to the reference template using a two-step process involving age-specific templates. ROI values for volume and DTI metrics were normalized to the median values of the 18-21-year group. Developmental trajectories were analyzed in two age segments; Segment 1: data between 0 and 2.69 years and Segment 2: for the remaining age range. Results:The results show that volumetric developmental trajectories varied largely among WM regions. The estimated volume at birth ranged: 12-53% of the adult value; where the rate of growth ranged: 3-30% of the adult value per year, in Segment 1; and 0-4% afterwards (Segment 2). The Corticospinal Tract, for example, showed protracted growth into young adulthood, while the Corpus Callosum growth was almost completed in the first 3 years. The magnitude of changes in local volume were generally larger than the magnitude of changes in diffusion metrics. Moreover, volumetric changes were more protracted, i.e., for many regions volume continued to increase even when diffusion metrics had reached a plateau. Conclusion:In conclusion, DTBM has shown developmental trajectories for WM volume in the human brain that are pathway specific and distinct from those obtained for DTI metrics. In future studies, DTBM should be performed in larger cohorts to assess correlation with cognitive and behavioral changes as well as establish ranges for normative values.

The landmark ongoing HEALthy Brain and Cognitive Development (HBCD) study will longitudinally chart brain development in a large sample (projected n =7,200) of infants through age 10 years with multimodal neuroimaging that includes an advanced diffusion MRI (dMRI) acquisition. Here, we detail advances in dMRI image processing developed for HBCD, incorporated into the widely used QSIPrep pipeline. Major changes to preprocessing include improvements in infant brain extraction, distortion correction, and normalization to infant-specific templates. Additionally, we describe a new software package – QSIRecon – that yields rich derived data including diverse maps of tissue microstructure as well as person-specific white matter bundles. Using dMRI data from a subset of the HBCD 1.0 release where age information was available ( n =529 sessions across two time points), we observe critical improvements in data quality with preprocessing and see expected developmental patterns. Moving forward, the publicly-available data from HBCD will rapidly grow to become the largest study of brain development in infancy and early childhood using dMRI. QSIPrep and QSIRecon are openly available and can be applied to other infant and pediatric dMRI datasets. ### Competing Interest Statement Andrew L. Alexander: Editor - Imaging Neuroscience, Scientific Advisor - ImgGyd, LLC Damien A. Fair is a patent holder on the Framewise Integrated Real-Time Motion Monitoring (FIRMM) software. He is also a co-founder of Turing Medical Inc that licenses this software. The nature of this financial interest and the design of the study have been reviewed by two committees at the University of Minnesota. They have put in place a plan to help ensure that this research study is not affected by the financial interest. Steven M. Nelson consults for Turing Medical, which commercializes FIRMM. This interest has been reviewed and managed by the University of Minnesota in accordance with its Conflict of Interest policies. National Institutes of Health, https://ror.org/01cwqze88, U01DA055371, U24DA055330, U01DA055350, U01DA055365, U01DA055370, U01DA055362

Diffusion MRI (dMRI) data suffer from a number of artifacts, including, but not limited to, low SNR, Gibbs ringing, bulk subject motion, within volume motion, eddy-current distortions, susceptibility-induced EPI distortions, and ghost artifacts. Appropriate pre-processing of diffusion-weighted images prior to model fitting is vital for accurate quantitative analysis. Over the years, the nature of dMRI data has evolved (smaller voxel sizes, significantly larger number of volumes and b-values, wider variety of acquisition paradigms, etc.) as have the required processing tools. Additionally, very large multi-site dMRI studies, on potentially uncooperative subjects (young children, geriatric populations, patients with movement disorders, etc.), have increased the necessity for dMRI processing pipelines that are fast, robustly capable of handling a variety of artifacts/distortions, and that have summary reporting capabilities to pinpoint problematic data. TORTOISE (Tolerably Obsessive Registration and Tensor Optimization Indolent Software Ensemble) (www.tortoisedti.org) has been redesigned, made adaptable and significantly enriched to meet these needs.

In magnetic resonance imaging (MRI), susceptibility-induced distortions pose a significant challenge for images acquired using echo planar imaging (EPI). Classical methods use two EPI images acquired in reverse phaseencoding (PE) directions to correct susceptibility artifacts. However, these methods suffer from long computation times, making them impractical for clinical usage. Recently, deep learning-based approaches have been proposed to enable a leap in computation efficiency for EPI susceptibility artifact correction. A vital consideration in reverse-PE-based correction is the need to take into account any potential subject motion between reversed-PE acquisitions. In this work, we propose an alignment-guided forward distortion network (agFD-Net) that accounts for subject motion during correction of susceptibility artifacts. Similar to its predecessor FD-Net, agFD-Net is trained in a physics-driven unsupervised fashion to estimate a single corrected image and a displacement field. In agFD-Net, a new pre-trained alignment network called AlignNet is plugged into the network architecture to facilitate motion correction. The results on experimental NIH dataset featuring realistic levels of motion demonstrate that agFD-Net provides rapid and high-fidelity artifact correction, while successfully accounting for subject motion.Clinical Relevance—EPI is the most commonly used sequence for diffusion MRI and functional MRI. While susceptibility artifacts in EPI require correction before any downstream evaluation, the long computation time of classical correction methods make them impractical for use in clinical settings. The proposed agFD-Net provides more than two orders of magnitude speed up in computational efficiency, making it a highly promising approach for use in clinical settings.

BACKGROUND AND OBJECTIVES:Diffusion tensor-based morphometry (DTBM) provides a more accurate assessment of volumetric changes in white matter structures than conventional T1-based TBM techniques. We sought to determine whether DTBM could detect volume loss in the corticospinal tract (CST) and whether this marker was associated with impaired stroke recovery. METHODS:Retrospective clinical MRI scans were obtained from a cohort of participants enrolled in a natural history study with acute anterior circulation ischemic stroke and unilateral arm impairment (NIH Stroke Scale [NIHSS] arm motor item score ≥2). Maps of the change in fractional anisotropy (delta FA) and the DTBM log of the Jacobian (LnJ, representing volumetric change) between scans, acquired <36 hours and 30 days after stroke, were computed. Voxel-wise Spearman rank-based analysis identified clustered regions of interest (ROIs) where the delta FA and LnJ from baseline to 30 days correlated with individual arm motor recovery scores on the NIHSS from baseline to 30 days. Qualitative comparisons were made between delta FA and LnJ maps for good and poor recovery groups (delta NIHSS arm item score ≥2 or <2, respectively) in reference to controls. RESULTS:Twenty-one participants with anterior circulation stroke were evaluated (mean age 63.6 years, median NIHSS arm motor item score 4, 48% female). Voxel-wise statistical maps identified 2 ROIs for delta FA and 4 ROIs for LnJ showing strong correlations with arm motor recovery (range of Spearman ρ = 0.77-0.81, all p < 0.01). The delta FA ROIs included the corona radiata and adjacent white matter, whereas LnJ ROIs included the centrum semiovale, corona radiata, internal capsule, and pons. Visual inspection of the average delta FA map from participants with poor arm motor recovery showed diffuse changes in the CST and adjacent subcortical regions while LnJ maps demonstrated more focal CST changes, particularly in the brainstem and internal capsule. DISCUSSION:DTBM detects focal volume loss in the CST over the first 30 days after stroke likely related to Wallerian degeneration. These volumetric changes may provide complementary information to FA in characterizing white matter loss after stroke. Like FA, DTBM shows strong correlations with arm motor recovery that could be useful for predicting recovery after stroke.

Purpose To propose and develop an image processing-based methodology for detecting and correcting residual Nyquist ghost artifacts in echo planar imaging (EPI), specifically using non-diffusion-weighted (b=0s/mm2) images acquired with four distinct phase-encoding directions (PEDs) in diffusion MRI. Approach Previous studies have demonstrated that acquiring images with four different PEDs can improve the reproducibility of diffusion derived quantitative maps. This improvement is achieved by averaging across PEDs to reduce the impact of residual EPI Nyquist ghost artifacts. These residual artifacts originate from imperfect ghost correction applied during image reconstruction and often persist into the preprocessing pipeline, potentially biasing downstream analyses. Building on these observations, the proposed method further improves this 4-way encoding approach by leveraging the properties of signal distributions to detect artifactual regions, specifically in non-diffusion weighted (b=0s/mm2). While the corrections are applied only to the b=0 images, its effectiveness is evaluated through downstream diffusion tensor estimation, by assessing the improvements in diffusion-derived metrics such as FA and MD. Additionally, the method can be tailored for specific artifact manifestations by considering their localization due to underlying acquisition parameters. Results Simulations with known ground-truth images demonstrated high artifact detection accuracy, achieving a Dice score of 0.91 for reconstructions without parallel imaging. In the in-vivo dataset, the method also improved longitudinal reproducibility, reducing variability by 30 % in ghost-affected regions. Conclusion The proposed correction method effectively detected and corrected residual ghost artifacts without the need of any additional k-space data, specifically in non-diffusion weighted images. This retrospective approach can be directly integrated into existing processing pipelines to further improve the quality of EPI images and enhancing image quality in studies that utilize 4-way PEDs acquisition.

Importance:Since 2015, US government and related personnel have reported dizziness, pain, visual problems, and cognitive dysfunction after experiencing intrusive sounds and head pressure. The US government has labeled these anomalous health incidents (AHIs).Objective:To assess whether participants with AHIs differ significantly from US government control participants with respect to clinical, research, and biomarker assessments.Design, Setting, and Participants:Exploratory study conducted between June 2018 and July 2022 at the National Institutes of Health Clinical Center, involving 86 US government staff and family members with AHIs from Cuba, Austria, China, and other locations as well as 30 US government control participants.Exposures:AHIs.Main Outcomes and Measures:Participants were assessed with extensive clinical, auditory, vestibular, balance, visual, neuropsychological, and blood biomarkers (glial fibrillary acidic protein and neurofilament light) testing. The patients were analyzed based on the risk characteristics of the AHI identifying concerning cases as well as geographic location.Results:Eighty-six participants with AHIs (42 women and 44 men; mean [SD] age, 42.1 [9.1] years) and 30 vocationally matched government control participants (11 women and 19 men; mean [SD] age, 43.8 [10.1] years) were included in the analyses. Participants with AHIs were evaluated a median of 76 days (IQR, 30-537) from the most recent incident. In general, there were no significant differences between participants with AHIs and control participants in most tests of auditory, vestibular, cognitive, or visual function as well as levels of the blood biomarkers. Participants with AHIs had significantly increased fatigue, depression, posttraumatic stress, imbalance, and neurobehavioral symptoms compared with the control participants. There were no differences in these findings based on the risk characteristics of the incident or geographic location of the AHIs. Twenty-four patients (28%) with AHI presented with functional neurological disorders.Conclusions and Relevance:In this exploratory study, there were no significant differences between individuals reporting AHIs and matched control participants with respect to most clinical, research, and biomarker measures, except for objective and self-reported measures of imbalance and symptoms of fatigue, posttraumatic stress, and depression. This study did not replicate the findings of previous studies, although differences in the populations included and the timing of assessments limit direct comparisons.

Background Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non‐Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. Purpose To confirm non‐Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile‐based method, “Pscore” to address this issue. Study Type Retrospective cohort. Population Nine hundred and sixty‐one healthy young adults (age: 22–35 years, females: 53%) from the Human Connectome Project. Field Strength/Sequence 3‐T, spin‐echo diffusion echo‐planar imaging, T1‐weighted: MPRAGE. Assessment The dMRI data were preprocessed using the TORTOISE pipeline. Forty‐eight regions of interest (ROIs) from the JHU atlas were redrawn on a study‐specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate “Pscores”—which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. Statistical Tests ROI‐wise distributions were assessed using log transformations, Zscore, and the “Pscore” methods. The percentages of extreme values above‐95th and below‐5th percentile boundaries (PEV >95 (%), PEV <5 (%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations. Results The dMRI metric distributions were systematically non‐Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV >95 ≠ 5%, PEV <5 ≠ 5%) whereas “Pscore” distributions were symmetric and balanced (PEV >95 = PEV <5 = 5%); even for small bootstrapped samples (average [SD]). Data Conclusion The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed “Pscore” method may help estimating individual deviations more accurately in skewed normative data, even from small datasets. Level of Evidence 1 Technical Efficacy Stage 1

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study’s core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω, in addition to the diffusion tensor, D, and relaxation, R1, R2, correlations. A D(ω)-R1-R2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D(ω)-R1-R2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

In this work, we have developed a quantitative diffusion MRI (dMRI) pipeline, to include acquisition, processing, and analysis, robust enough to evaluate the evolution of a neurodegenerative disease in each individual patient.

The Human Connectome Project (HCP) has brought significant advancements in hardware, acquisition, and preprocessing. Even after a decade since its collection, the HCP diffusion MRI data is still relevant for its richness and high resolution. Noise and geometric distortions, however, are particularly pronounced in this dataset. In this work, we have reprocessed nearly the entire HCP dMRI dataset while applying several recent processing improvements. We compared the quality of the newly processed dMRI outputs to the release version. We observed clearly detectable improvements. The data originated from this new processing will be made publicly available.

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

Background: Achieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. Purpose: Magnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study type: Prospective observational study. Subjects: A spherical isotropic diffusion phantom, and a single human control volunteer. Field strength/sequence: 3 T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm(2) with 12, 32, and 384 diffusion gradient directions per shell. Assessment: We compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical tests: Wilcoxon rank-sum test between uncorrected and corrected data. Results: In phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p < 0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p < 0.05). Conclusion: Our method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

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.

Quantitative magnetic resonance imaging (MRI) investigations of brain anatomy in children and young adults with Down syndrome (DS) are limited, with no diffusion tensor imaging (DTI) studies covering that age range. We used DTI-driven tensor based morphometry (DTBM), a novel technique that extracts morphometric information from diffusion data, to investigate brain anatomy in 15 participants with DS and 15 age- and sex-matched typically developing (TD) controls, ages 6–24 years (mean age ~17 years). DTBM revealed marked hypoplasia of cerebellar afferent systems in DS, including fronto-pontine (middle cerebellar peduncle) and olivo-cerebellar (inferior cerebellar peduncle) connections. Prominent gray matter hypoplasia was observed in medial frontal regions, the inferior olives, and the cerebellum. Very few abnormalities were detected by classical diffusion MRI metrics, such as fractional anisotropy and mean diffusivity. Our results highlight the potential importance of cerebro-cerebellar networks in the clinical manifestations of DS and suggest a role for DTBM in the investigation of other brain disorders involving white matter hypoplasia or atrophy.

Purpose: To assess the effects of blip-up and -down echo planar imaging (EPI) acquisition designs, with different choices of phase-encoding directions (PEDs) on the reproducibility of diffusion MRI (dMRI)-derived metrics in the human brain. Methods: Diffusion MRI data in seven subjects were acquired five times, each with five different protocols. The base design included 64 diffusion directions acquired with anterior-posterior (AP) PED, the first and second protocols added reverse phase-encoded b=0 s/mm(2) posterior-anterior (PA) PED images. The third one included 32 directions all with PED acquisitions with opposite polarity (AP and PA). The fourth protocol, also with 32 unique directions used four PEDs (AP, PA, right-left (RL), and left-right (LR)). The scan time was virtually identical for all protocols. The variability of diffusion MRI metrics for each subject and each protocol was computed across the different sessions. Results: The highest reproducibility for all dMRI metrics was obtained with protocol four (AP/PA-RL/LR, ie, four-way PED). Protocols that used only b = 0 s/mm(2) for distortion correction, which are the most widely used designs, had the lowest reproducibility. Conclusions: An acquisition design with four PEDs, including all DWIs in addition to b =0 s/mm(2) images should be used to achieve high reproducibility in diffusion MRI studies.

Diffusion weighted MRI (DWMRI) and the myriad of analysis approaches (from tensors to spherical harmonics and brain tractography to body multi-compartment models) depend on accurate quantification of the apparent diffusion coefficient (ADC). Signal drift during imaging (e.g., due to b0 drift associated with heating) can cause systematic non-linearities that manifest as ADC changes if not corrected. Herein, we present a case study on two phantoms on one scanner. Different scan protocols exhibit different degrees of drift during similar scans and may be sensitive to the order of scans within an exam. Vos et al. recently reviewed the effects of signal drift in DWMRI acquisitions and proposed a temporal model for correction. We propose a novel spatial-temporal model to correct for higher order aspects of the signal drift and derive a statistically robust variant. We evaluate the Vos model and propose a method using two phantoms that mimic the ADC of the relevant brain tissue (0.36-2.2 x 10-3 mm(2)/s) on a single 3 T scanner. The phantoms are (1) a spherical isotropic sphere consisting of a single concentration of polyvinylpyrrolidone (PVP) and (2) an ice-water phantom with 13 vials of varying PVP concentrations. To characterize the impact of interspersed minimally weighted volumes ("b0's"), image volumes with b-value equal to 0.1 s/mm(2) are interspersed every 8, 16, 32, 48, and 96 diffusion weighted volumes in different trials. Signal drift is found to have spatially varying effects that are not accounted for with temporal-only models. The novel model captures drift more accurately (i.e., reduces the overall change per-voxel over the course of a scan) and results in more consistent ADC metrics.

Diffusion weighted MRI (DW-MRI) depends on accurate quantification signal intensities that reflect directional apparent diffusion coefficients (ADC). Signal drift and fluctuations during imaging can cause systematic non-linearities that manifest as ADC changes if not corrected. Here, we present a case study on a large longitudinal dataset of typical diffusion tensor imaging. We investigate observed variation in the cerebral spinal fluid (CSF) regions of the brain, which should represent compartments with isotropic diffusivity. The study contains 3949 DW-MRI acquisitions of the human brain with 918 subjects and 542 with repeated scan sessions. We provide an analysis of the inter-scan, inter-session, and intra-session variation and an analysis of the associations with the applied diffusion gradient directions. We investigate a hypothesis that CSF models could be used in lieu of an interspersed minimally diffusion-weighted image (b0) correction. Variation in CSF signal is not largely attributable to within-scan dynamic anatomical changes (3.6%), but rather has substantial variation across scan sessions (10.6%) and increased variation across individuals (26.6%). Unfortunately, CSF intensity is not solely explained by a main drift model or a gradient model, but rather has statistically significant associations with both possible explanations. Further exploration is necessary for CSF drift to be used as an effective harmonization technique.

We describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.