Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time

Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but remains challenging due to low signal-to-noise ratio (SNR), motion artifacts, and ongoing myelination. In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. We predict the Fiber Orientation Distribution (FOD) from multi-shell dMRI signals acquired with a reduced set of gradient directions (30% of the full protocol), enabling faster and more cost-effective acquisitions. We train and evaluate the performance of our sCNN using real data from 43 neonatal dMRI datasets provided by the Developing Human Connectome Project (dHCP). Our results demonstrate that the sCNN achieves significantly lower mean squared error (MSE) and higher angular correlation coefficient (ACC) compared to a Multi-Layer Perceptron (MLP) baseline, indicating improved accuracy in FOD estimation. Furthermore, tractography results based on the sCNN-predicted FODs show improved anatomical plausibility, coverage, and coherence compared to those from the MLP. These findings highlight that sCNNs, with their inherent rotational equivariance, offer a promising approach for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development.

HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI

Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord

Multiple sclerosis is an inflammatory disorder of the central nervous system. Quantitative MRI has huge potential to provide intrinsic and normative values of tissue properties useful for diagnosis, prognosis and ultimately clinical follow-up of this disease. However, there is a large discrepancy between the clinical observations and how the pathology is exhibited in MRI brain scans. Complementary to brain imaging, the study of multiple sclerosis lesions in the spinal cord has recently gained interest as a potential marker for early physical impairment. Therefore, investigating how the spinal cord is damaged using quantitative imaging, in particular, diffusion MRI, becomes an acute challenge. In this work, we extract average diffusion MRI metrics per vertebral level from spinal cord data acquired from multiple clinical sites. The diffusion-based metrics involved are extracted from the diffusion tensor imaging and Ball-and-Stick models and quantified for every cervical vertebral level using a collection of image processing methods and an atlas-based approach. Then, we perform a statistical analysis study to characterize the feasibility of these metrics to detect lesions. Specifically, we study the usefulness of combining different metrics to improve the accuracy prediction score associated with the presence of multiple sclerosis lesions. We demonstrate the grade of sensitivity to underlying microstructure changes in MS patients of each metric. Ball-and-Stick provides novel information about the MS damage to tissue microstructure. In addition, we show that choosing a subset of metrics: [FA, RD, MD] and [FWW, MD, Stick-AD, RD], which bring complementary information, has significantly increased the prediction score of the presence of the MS lesion in the cervical spinal cord.

Evaluation of distortion correction methods in diffusion MRI of the spinal cord

Background: Magnetic field inhomogeneities generate important geometric distortions in reconstructed echo-planar images. Various procedures were proposed for correcting these distortions on brain images; yet, few neuroimaging studies tailored and incorporated the use of these techniques in spinal cord diffusion MRI. Purpose: We present a comparative evaluation of distortion correction methods that use the reversed gradient polarity technique on spinal cord. We propose novel geometric metrics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spinal cord. Subjects: 95 subjects, among which 29 healthy controls and 66 multiple sclerosis patients. Assessment: Geometric distortions were corrected using 4 state-of-the-art methods. We measured the alignment of the principal direction of diffusion with the apparent centerline of the spine after correction and the correlation with the reference anatomical image. Results are computed per vertebral level, to evaluate the impact on different portions of the spine. Besides, subjective evaluation of the quality of the correction of healthy subjects images was performed by three expert raters. Results: As a result of distortion correction, the diffusion directions are better aligned locally with the centerline, in particular at both ends of the acquisition window. The cross-correlation with anatomical image is also improved by Hyperelastic Susceptibility Artefact Correction (HySCO) and block-matching. The subjective evaluation for HySCO is significantly better (p < 0.05) than for Block-Matching; TOPUP performs significantly worse than the three other methods. Conclusion: Correction based on HySCO provide best results among the selected methods.

Reproducibility and Evolution of Diffusion MRI Measurements within the Cervical Spinal Cord in Multiple Sclerosis

In Multiple Sclerosis (MS), there is a large discrepancy between the clinical observations and how the pathology is exhibited on brain images, this is known as the clinical-radiological paradox (CRP). One of the hypotheses is that the clinical deficit may be more related to the spinal cord damage than the number or location of lesions in the brain. Therefore, investigating how the spinal cord is damaged becomes an acute challenge to better understand and overcome the CRP. Diffusion MRI is known to provide quantitative figures of neuronal degeneration and axonal loss, in the brain as well as in the spinal cord. In this paper, we propose to investigate how diffusion MRI metrics vary in the different cervical regions with the progression of the disease. We first study the reproducibility of diffusion MRI on healthy volunteers with a test-retest procedure using both standard diffusion tensor imaging (DTI) and multi-compartment Ball-and-Stick models. Then, based on the test re-test quantitative calibration, we provide quantitative figures of pathology evolution between M0 and M12 in the cervical spine on a set of 31 MS patients, exhibiting how the pathology damage spans in the cervical spinal cord.