Biology subjects

Rathi, Y.

Publications and source records attributed to Rathi, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Joint reconstruction of multi-contrast relaxation-diffusion imaging

Diffusion MRI across multiple gradient directions, b-values and echo times (TEs) provides complementary tissue contrast but requires extensive sampling. Joint k-space and q-space undersampling requires reconstruction that preserves diffusion- and TE-dependent signal variation while exploiting shared anatomical information. Here we introduce DIFFUSE (Diffusion-relaxometry Implicit Field Fitting from Undersampled k-q Space across Echoes), a subject-specific joint k-q-TE image reconstruction framework. It fits a single implicit neural representation (INR) directly to complementary undersampled multi-coil k-space measurements across diffusion encodings and echo times. The INR (a self-supervised neural network) maps spatial location, b-value and TE to baseline b0 signals and spherical harmonic coefficients for directional diffusion-weighted image reconstruction. The measurement-specific forward model incorporates phase, coil sensitivities and motion and eddy current related transforms. We evaluated DIFFUSE using retrospective Human Connectome Project data, controlled motion simulations and in vivo multi-shell and multi-TE acquisitions. At sixfold in-plane acceleration, DIFFUSE reduced normalized mean squared error by 57-73% relative to SENSE across two diffusion shells in the retrospective experiment. In the controlled motion experiment, motion-aware DIFFUSE reduced normalized mean squared error by 66-72% relative to static DIFFUSE. The prospective study demonstrated multi-shell reconstruction at 1.3 mm isotropic resolution with significantly high signal-to-noise ratio (SNR). In a separate multi-TE experiment, a single representation jointly reconstructed all nine contrasts corresponding to 3 b-value/TE combinations. DIFFUSE thus provides a common reconstruction strategy for accelerated protocols spanning multiple diffusion encodings and echo times.

neuroscience↗

bioRxiv2026

High-SNR Whole-Brain Mesoscale Diffusion MRI Using Rotating-View Acquisitions (ROVER-dMRI)

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

neuroscience↗

bioRxiv2026

Test-Retest Reproducibility of Single- and Cross-Population White Matter Atlases in Diffusion MRI Tractography

Diffusion MRI tractography enables noninvasive mapping of white matter fiber tracts. Atlas-based white matter parcellation supports automated tract identification by assigning individual streamlines to atlas-defined clusters and anatomical tract labels. Because clustering-based white matter atlases are constructed from cohort-specific tractography data, they capture common white matter organization represented in the atlas-construction population. Although major white matter anatomy is shared across populations, subtle population-related anatomical variability may influence atlas representation and test-retest correspondence. Therefore, the reproducibility and cross-population generalizability of tractography-based white matter atlases are important considerations for quantitative neuroimaging studies. In this study, we evaluated whether incorporating cross-population anatomical variability during atlas construction improves test-retest reproducibility. To do so, we compared a single-population ORG atlas constructed from a Western cohort with the cross-population East-West White Matter Atlas constructed from both Eastern and Western cohorts. Test-retest diffusion MRI scans from the Human Connectome Project Young Adult (HCP-YA) dataset and the Connectivity-based Brain Imaging Research Database (C-BIRD) were analyzed as independent Western and Eastern test-retest cohorts, respectively. Whole-brain tractography was reconstructed for each dMRI scan and parcellated using both atlases. Reproducibility was assessed using tract detection rate at both cluster level and anatomical tract level, weighted Dice coefficient, and the relative difference of mean fractional anisotropy (FA). Both atlases showed stable tract detection across test-retest scans in both cohorts. Compared with the ORG atlas, the East-West White Matter Atlas achieved higher overall spatial overlap and lower test-retest variability in mean FA, although atlas performance varied across individual tracts. These findings suggest that integrating cross-population information during atlas construction improves the reproducibility and generalizability of white matter atlas mapping across independent populations and imaging protocols.

neuroscience↗

bioRxiv2026

Field-Correcting GRAPPA (FCG): a technique to correctspatiotemporal-varying phase errors in Echo Planar Imaging

PurposeTo develop a Field-Correcting GRAPPA (FCG) technique to correct the spatiotemporal-varying phase errors in EPI caused by eddy currents. MethodsThe fast-changing gradient in EPI causes strong eddy current effects and associated spatiotemporal-varying phase errors, producing significant image artifacts. The use of higher gradient amplitude, slew rate, and ramp sampling factor for faster imaging exacerbates this problem. In this work, FCG was developed to address this challenge by using a multi-layer perceptron (MLP) to provide a compact representation of a family of GRAPPA-like kernels that correct the spatiotemporal-varying phase errors in the data. A dedicated calibration pipeline was designed to acquire high-quality source and target data for MLP training in both slice-by-slice and simultaneous multi-slice (SMS) acquisitions. To validate FCGs assumptions and performance, a field camera was used to provide ground-truth measurement of phase patterns. The performance of FCG was further validated on phantom and in vivo experiments using demanding EPI trajectories across multiple 3T and 7T systems. ResultsField camera measurements revealed strong spatiotemporal phase variations along the kx direction that repeat along ky during EPI readouts. The experiments on high-performance systems across 3T and 7T demonstrate that FCG can provide superior correction for the artifacts induced by spatiotemporal-varying phase errors compared with existing approaches. ConclusionFCG is an effective and robust method for correcting spatiotemporal phase errors in EPI, enabling improved image quality on high-performance systems.

bioengineering↗

bioRxiv2026

MaxEnt-DTD: Maximum-Entropy Estimation of Diffusion Tensor Distribution for Fiber Orientation and Microstructure Characterization

Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution (DTD) framework jointly represents fiber orientation and microstructure, but estimating DTD from finite, noisy measurements is severely ill-posed. Existing inversion methods either rely on nonnegativity constrained basis representations, which are challenging to sale to high-dimensional and high-resolution distributions, or use sampling-based approaches with limited reliability. We propose MaxEnt-DTD, a maximum-entropy algorithm for DTD estimation from finite and noisy dMRI data. By deriving the Lagrange dual formulation, we reformulate a constrained infinite-dimensional optimization problem into a finite-dimensional unconstrained convex optimization problem, substantially reducing the parameter space and enabling tractable whole-brain DTD estimation. We evaluate MaxEnt-DTD using both synthetic and in vivo data from the Human Connectome Project protocol and a second dataset using advanced B-tensor diffusion encoding. We compare MaxEnt-DTD-derived fiber orientation distributions with results from CSD and Monte-Carlo inversion methods, and assess fiber-specific microstructure measures and rotation-invariant metrics based on the cumulants of DTD. The results demonstrate that MaxEnt-DTD provides a reliable and efficient framework for joint fiber-orientation and microstructure analysis in dMRI.

neuroscience↗

bioRxiv2026

Connectional neuroanatomy of U-fibers in the rhesus monkey brain

The superficial white matter (SWM), the region of white matter immediately beneath the gray matter-white matter (GM-WM) border, contains short cortico-cortical association fibers that interconnect neighboring cortical regions. The SWM is estimated to comprise the majority of axons in the cerebral white matter and is thus thought to play a major role in cortical information processing. Despite this prominence, and growing attention in the diffusion MRI (dMRI) field, the connectional organization of the SWM remains poorly defined. A major component of the SWM is U-shaped fiber bundles, classically depicted in brain dissections as bundles beneath sulci that interconnect adjacent gyri. The prevailing view, from both brain dissection and dMRI, considers U-fibers to be ubiquitous elements of the SWM that form symmetric connections between corresponding portions of adjacent gyri beneath all sulci, and they are routinely reconstructed in tractography as a layer of short association connections. However, their actual prevalence and organization in the primate brain have never been systematically evaluated with experimental tract tracing. To address this gap, we analyzed 28 archival macaque tract-tracing cases in which anterograde tracers were injected on gyri adjacent to major cortical sulci (intraparietal, central, principal, superior temporal), all of which have clear anatomical correspondences to sulci in the human cerebral cortex. Several organizational principles emerged. First, although short association fibers were present in every case, U-fibers - operationally defined as labeled fibers that left the injection site, passed beneath a sulcus, and terminated on the adjacent gyrus - were only observed in a minority of cases. Second, U-fiber incidence was sulcus-dependent: U-fibers were consistently present beneath the principal and central sulci, but uncommon beneath the intraparietal and superior temporal sulci. Third, when present, U-fibers did not follow a uniform trajectory - some took a course symmetric and orthogonal to the sulcus, whereas others followed oblique trajectories to more distant regions. In addition to U-fibers, we identified shorter association fiber bundles that terminated on the sulcal bank proximal to the injection, or on the opposing bank after they crossed the fundus of the sulcus, evidence that the SWM contains multiple classes of short association fiber bundles rather than a single canonical U-fiber system. We further identified a consistent 200-300 {micro}m band of white matter beneath cortical layer 6 that contained exclusively short association fibers on approach to their cortical terminations and was selectively avoided by deeper long cortico-cortical pathways. This band was present across all examined sulci, suggesting that it is a conserved feature of the cortical white matter. Notably, U-fibers were found both within this band and in the white matter below. We also identified deeper fiber bundles that adopted a U-shaped course beneath a sulcus but did not interconnect adjacent gyri. Thus, a U-shaped trajectory alone does not define a U-fiber. Together, these findings constitute the first systematic connectional study of cortical U-fibers in major sulci of the non-human primate brain and indicate that the white matter beneath the cortex is a highly complex system in which short association fiber bundle organization reflects the selective connectivity of the cortical areas separated by a sulcus, rather than a uniform U-fiber architecture imposed by the geometry of the overlying sulcus. These results have potential consequences for dMRI and anatomical studies of white matter and provide an anatomically grounded basis to better define SWM organization in anatomy and tractography.

neuroscience↗

bioRxiv2026

Anatomically constrained and curated cerebellar tractography (ACCURAT): an open framework and a pathway-specific neuroanatomical reference

Cerebellar pathways form extensive structural circuits linking the cerebellum with the brainstem, thalamus, and cerebrum, underlying motor, cognitive, and affective functions. Diffusion MRI tractography provides the only non-invasive method for mapping these pathways in vivo, but reconstruction of cerebellar connectivity remains challenging due to crossing fibers, peduncular bottlenecks, decussations, multi-synaptic circuits, and numerous small nuclei that define pathway origins and terminations. Here we introduce Anatomically Constrained and CURAted Tractography (ACCURAT), an open framework for reconstructing cerebellar pathways from diffusion MRI using anatomical priors and rule-based streamline queries. ACCURAT combines anatomical segmentation, densely seeded tractography, and vertex-level evaluation of anatomical constraints along streamline trajectories, enabling the isolation of pathway segments within specific nuclei while preventing their propagation across synaptic boundaries. To define these constraints, we provide a concise, pathway-by-pathway synthesis of cerebellar connectional anatomy based on experimental tract-tracing literature and organized for tractography applications. We identify pathway-specific origins, trajectories, terminations, decussation patterns, and tractography challenges, and use this information to inform tractography-ready cerebellar pathway definitions. Using ultra-high-resolution submillimeter diffusion MRI (0.76 mm gSlider acquisition) from healthy participants, we reconstruct multiple extrinsic and intrinsic cerebellar pathways, including specific components of the inferior, middle, and superior cerebellar peduncles; challenging decussating pathways such as the olivocerebellar and dentato-olivary projections; and intrinsic cerebellar pathways, including Purkinje corticonuclear projections and intracortical parallel fibers. ACCURAT generalizes across tractography algorithms, producing comparable reconstructions with both probabilistic parallel transport tractography and deterministic unscented Kalman filter tractography. Together, the ACCURAT framework and accompanying neuroanatomical reference provide an anatomically grounded, tractography-oriented resource for reconstructing cerebellar pathways in vivo and for supporting future development and evaluation of cerebellar tractography methods.

neuroscience↗

bioRxiv2025

Study of Sex Differences in the Whole Brain White Matter Using Diffusion MRI Tractography and Suprathreshold Fiber Cluster Statistics

Sex-specific characteristics demonstrate a substantial influence on the brain white matter (WM), suggesting distinct structural connectivity patterns between females and males. Diffusion MRI (dMRI) tractography is an important tool in assessing WM connectivity and brain tissue microstructure across different populations. Whole brain tractography analysis for group statistical comparison is a challenging task due to the large number of WM connections. This work studies whole-brain WM connectivity differences between females and males using dMRI tractography. We study a large cohort of 707 subjects from the Human Connectome Project Young Adult dataset. By applying a well-established fiber clustering pipeline and a suprathreshold fiber cluster statistical method, we analyze tracts in the cerebral cortex and understudied pathways like those connecting to the cerebellum. We identify several tracts with significant sex differences in terms of their fractional anisotropy and/or mean diffusivity. These include deep tracts like the arcuate fasciculus, corticospinal tract, and corpus callosum, superficial tracts in the frontal lobe, and cerebellar tracts. Finally, correlation analysis reveals that these WM differences are linked to a range of neurobehavioral measures, with the strongest and most consistent associations observed for motor function, suggesting motor circuits as a potential key focus for future research.

neuroscience↗

bioRxiv2025

The superficial white matter in language processing: Broca's area connections are bilaterally associated with individual performance in children and adults

While deeper white matter connections, such as the arcuate fasciculus and frontal aslant tract, are well known for their role in language and show leftward asymmetries in adults, the contribution of the short-range cortico-cortical superficial white matter (SWM) connections remains less understood. In this preregistered study, we examined white matter connections of Brocas area and its right hemisphere homolog in early adolescents and young adults using two large, publicly available datasets: the Adolescent Brain Cognitive Development Study and the Human Connectome Project Young Adult Study, totaling over 10,000 participants. We anatomically curated the ODonnell Research Group (ORG) tractography atlas to identify SWM fiber clusters intersecting Brocas area (pars opercularis and pars triangularis), confirmed through expert visual inspection. We investigated the microstructure, structural connectivity, and lateralization of Brocas area SWM and its relationship with language performance (Picture Vocabulary and Oral Reading Recognition assessments), in comparison to the deeper white matter connections of the frontal aslant tract and arcuate fasciculus. The arcuate fasciculus demonstrated the strongest and most consistent leftward lateralization of both microstructure (fractional anisotropy, FA) and structural connectivity (number of streamlines, NoS), with structure-function associations that were bilateral in adolescents and left-dominant in adults. Interestingly, despite weaker lateralization than the arcuate fasciculus, both the SWM and the frontal aslant tract demonstrated comparable associations with language performance. The frontal aslant tract showed greater leftward lateralization (FA and NoS) with age and was bilaterally associated with language performance, particularly in adolescents. Compared to these deeper tracts, Brocas area SWM demonstrated left-lateralized FA and right-lateralized NoS in both adolescents and adults, with stronger FA lateralization in adults. Bilateral SWM FA and NoS were significantly associated with language performance in both hemispheres and age groups. Overall, these results suggest that Brocas area SWM may support language in a more bilaterally distributed manner and highlight the importance of considering SWM connections in studies of language development and neurosurgical planning.

neuroscience↗

bioRxiv2025

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation

The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI. Key pointsO_LIWe provide evidence about the performance of different dMRI contrasts for cerebellar structure segmentation using a deep learning neural network. C_LIO_LIThe diffusion MRI spherical mean provides improved and stable cerebellar structure segmentation performance. C_LIO_LIThe spherical mean is easy to compute and can be used for cerebellar structure segmentation on retrospective clinical diffusion MRI data. C_LI

neuroscience↗

bioRxiv2025

Consistent cerebellar pathway-cognition associations across pre-adolescents & young adults: a diffusion MRI study of 9000+ participants

The cerebellum, long implicated in movement, is now recognized as a contributor to higher-order cognition. The cerebellar pathways provide key structural links between the cerebellum and cerebral regions integral to language, memory, and executive function. Here, we present a large-scale, cross-sectional diffusion MRI (dMRI) analysis investigating the relationships between cerebellar pathway microstructure and cognitive performance in over 9,000 participants spanning pre-adolescence (n>8,000 from the ABCD dataset) and young adulthood (n>900 from the HCP-YA dataset). We assessed the microstructure of five cerebellar pathways--the inferior, middle, and superior cerebellar peduncles; the parallel fibers; and input/Purkinje fibers--using three dMRI measures of fractional anisotropy, mean diffusivity, and number of streamlines. Cognitive performance was evaluated using seven NIH Toolbox assessments of language, executive function, and memory. In both datasets, we found numerous significant associations between cerebellar pathway microstructure and cognitive performance. These associations showed a strong correlation across the two datasets (r = 0.47, p < 0.0001), underscoring the reliability of cerebellar dMRI-cognition relationships in pre-adolescents and young adults. In both datasets, the strongest associations were found between the superior cerebellar peduncle and performance on language assessments, suggesting this pathway plays an important role in language function across age groups. In young adults, but not pre-adolescents, parallel fiber microstructure was linked to inhibitory control, suggesting that contributions to attentional processes may emerge or strengthen with maturation. Overall, our findings highlight the important role of cerebellar pathways in cognition and the utility of large-scale datasets for advancing our understanding of brain-cognition relationships. SignificanceThis study provides strong evidence linking cerebellar pathway tissue microstructure to cognition across large populations of preadolescent children and young adults. By leveraging diffusion MRI tractography and cognitive performance data from two major datasets, we identify significant relationships between cerebellar pathway microstructure and cognitive performance. Importantly, findings are significantly correlated across datasets, pointing to the consistency of these relationships, bridging age groups and acquisitions. These results highlight the cerebellar pathways integral role in cognitive functioning and underscore the value of large-scale, population-based studies in advancing our understanding of brain-cognition relationships. Classification 1) Biological Sciences, Neuroscience; 2) Social Sciences, Psychological and Cognitive Sciences

neuroscience↗

bioRxiv2024

Fiber Microstructure Quantile (FMQ) Regression: A Novel Statistical Approach for Analyzing White Matter Bundles from Periphery to Core

The structural connections of the brains white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.g., cognitive scores). Our approach analyzes tissue microstructure measures based on quantile-specific bundle regions. These regions are defined in a data-driven fashion according to the quantiles of fractional anisotropy (FA) of a population fiber bundle, which pools all individuals bundles. The FA quantiles induce a natural subdivision of a fiber bundle, defining regions from the periphery (low FA) to the core (high FA) of the population fiber bundle. To investigate how fiber bundle tissue microstructure relates to covariates of interest, we employ the statistical technique of quantile regression. Unlike ordinary regression, which only models a conditional mean, quantile regression models the conditional quantiles of a response variable. This enables the proposed analysis, where a quantile regression is fitted for each quantile-specific bundle region. To demonstrate FMQ Regression, we perform an illustrative study in a large healthy young adult tractography dataset derived from the Human Connectome Project-Young Adult (HCP-YA), focusing on particular bundles expected to relate to particular aspects of cognition and motor function. In comparison with traditional regression analyses based on FA Mean and Automated Fiber Quantification (AFQ), we find that FMQ Regression provides a superior model fit with the lowest mean squared error. This demonstrates that FMQ Regression captures the relationship between scalar factors and white matter microstructure more effectively than the compared approaches. Our results suggest that FMQ Regression, which enables FA analysis in data-driven regions defined by FA quantiles, is more powerful for detecting brain-behavior associations than AFQ, which enables FA analysis in regions defined along the trajectory of a bundle. FMQ Regression finds significant brain-behavior associations in multiple bundles, including findings unique to males or to females. In both males and females, language performance is significantly associated with FA in the left arcuate fasciculus, with stronger associations in the bundles periphery. In males only, memory performance is significantly associated with FA in the left uncinate fasciculus, particularly in intermediate regions of the bundle. In females only, motor performance is significantly associated with FA in the left and right corticospinal tracts, with a slightly lower relationship at the bundle periphery and a slightly higher relationship toward the bundle core. No significant relationships are found between executive function and cingulum bundle FA. Our study demonstrates that FMQ Regression is a powerful statistical approach that can provide insight into associations from bundle periphery to bundle core. Our results also identify several brain-behavior relationships unique to males or to females, highlighting the importance of considering sex differences in future research.

neuroscience↗

bioRxiv2024

Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements

PurposeTo compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. MethodThe ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). ResultsTDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4x larger NRMSEs for phantom, 2x for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5x larger bias in the covariance between relaxation-diffusion coefficients. ConclusionOur analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

neuroscience↗

bioRxiv2024

Cross-site harmonization of diffusion MRI data without matched training subjects

PurposeDiffusion MRI (dMRI) data typically suffer of significant cross-site variability, which prevents naively performing pooled analyses. To attenuate cross-site variability, harmonization methods such as the rotational invariant spherical harmonics (RISH) have been introduced to harmonize the dMRI data at the signal level. A common requirement of the RISH method, is the availability of healthy individuals who are matched at the group level, which may not always be readily available, particularly retrospectively. In this work, we propose a framework to harmonize dMRI without matched training groups. MethodsOur framework learns harmonization features while controlling for potential covariates using a voxel-based generalized linear model (RISH-GLM). RISH-GLM allows to simultaneously harmonize data from any number of sites while also accounting for covariates of interest, thus not requiring matched training subjects. Additionally, RISH-GLM can harmonize data from multiple sites in a single step, whereas RISH is performed for each site independently. ResultsWe considered data of training subjects from retrospective cohorts acquired with 3 different scanners and performed 3 harmonization experiments of increasing complexity. First, we demonstrate that RISH-GLM is equivalent to conventional RISH when trained with data of matched training subjects. Secondly, we demonstrate that RISH-GLM can effectively learn harmonization with two groups of highly unmatched subjects. Thirdly, we evaluate the ability of RISH-GLM to simultaneously harmonize data from 3 different sites. DiscussionRISH-GLM can learn cross-site harmonization both from matched and unmatched groups of training subjects, and can effectively be used to harmonize data of multiple sites in one single step.

bioinformatics↗

bioRxiv2024

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

neuroscience↗

bioRxiv2023

Harmonized diffusion MRI data and white matter measures from the baseline Adolescent Brain Cognitive Development (ABCD) study

The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking [~]50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

neuroscience↗

bioRxiv2023

A domain-agnostic MR reconstruction framework using a randomly weighted neural network

PurposeTo design a randomly-weighted neural network that performs domain-agnostic MR image reconstruction from undersampled k-space data without the need for ground truth or extensive in-vivo training datasets. The network performance must be similar to the current state-of-the-art algorithms that require large training datasets. MethodsWe propose a Weight Agnostic randomly weighted Network method for MRI reconstruction (termed WAN-MRI) which does not require updating the weights of the neural network but rather chooses the most appropriate connections of the network to reconstruct the data from undersampled k-space measurements. The network architecture has three components, i.e. (1) Dimensionality Reduction Layers comprising of 3d convolutions, ReLu, and batch norm; (2) Reshaping Layer is Fully Connected layer; and (3) Upsampling Layers that resembles the ConvDecoder architecture. The proposed methodology is validated on fastMRI knee and brain datasets. ResultsThe proposed method provides a significant boost in performance for structural similarity index measure (SSIM) and root mean squared error (RMSE) scores on fastMRI knee and brain datasets at an undersampling factor of R=4 and R=8 while trained on fractal and natural images, and fine-tuned with only 20 samples from the fastMRI training k-space dataset. Qualitatively, we see that classical methods such as GRAPPA and SENSE fail to capture the subtle details that are clinically relevant. We either outperform or show comparable performance with several existing deep learning techniques (that require extensive training) like GrappaNET, VariationNET, J-MoDL, and RAKI. ConclusionThe proposed algorithm (WAN-MRI) is agnostic to reconstructing images of different body organs or MRI modalities and provides excellent scores in terms of SSIM, PSNR, and RMSE metrics and generalizes better to out-of-distribution examples. The methodology does not require ground truth data and can be trained using very few undersampled multi-coil k-space training samples.

neuroscience↗

bioRxiv2022

Characterization of b-value dependent T2 relaxation rates for probing neurite microstructure

Brain tissue microstructure is characterized by heterogeneous diffusivity and transversal relaxation rates. Standard diffusion MRI (dMRI) is acquired using a single echo time (TE) and only provides information about heterogeneous diffusivity in the underlying tissue. Combined relaxation diffusion MRI (rdMR) integrates dMRI with multiple TEs to probe the coupling between relaxation rate and diffusivity. This work introduces a method to model rdMRI data signals by characterizing the apparent relaxation rate related to dMRI with different b-values. The proposed approach can extrapolate dMRI signals to ultra-long or ultra-short TEs to increase or reduce signals from intra-neurite water to improve the characterization of neurite microstructure without solving multi-compartment models. The performance of the proposed method was examined using an in vivo dataset acquired from a clinical scanner to estimate neurite sizes.

neuroscience↗