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ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Brain Imaging Methods

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1604545

This article is part of the Research TopicBrain Metabolic Imaging by Magnetic Resonance Imaging and Spectroscopy: Methods and Clinical Applications Volume IIView all 5 articles

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

Provisionally accepted
  • Boston Children's Hospital, Harvard Medical School, Boston, United States

The final, formatted version of the article will be published soon.

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 significantly outperforms a Multi-Layer Perceptron (MLP) baseline across multiple quantitative metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Angular Correlation Coefficient (ACC), angular error, and peak match rate, indicating superior FOD estimation accuracy. More importantly, it yields FODs and tractography that are quantitatively comparable and qualitatively highly similar to those from a reliable Hybrid-CSD ground truth, despite using only 30% of the full acquisition data. These findings highlight sCNNs' potential for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development with shorter scan times.

Keywords: diffusion MRI, Spherical CNNs, neonatal brain, Fiber Orientation, Geometric deep learning, tractography

Received: 02 Apr 2025; Accepted: 09 Jul 2025.

Copyright: © 2025 Snoussi and Karimi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Haykel Snoussi, Boston Children's Hospital, Harvard Medical School, Boston, United States

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