Neural Spherical Harmonics for Structurally Coherent Continuous Representation of Diffusion MRI Signal

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We present a novel way to model diffusion magnetic resonance imaging (dMRI) datasets, that benefits from the structural coherence of the human brain while only using data from a single subject. Current methods model the dMRI signal in individual voxels, disregarding the intervoxel coherence that is present. We use a neural network to parameterize a spherical harmonics series (NeSH) to represent the dMRI signal of a single subject from the Human Connectome Project dataset, continuous in both the angular and spatial domain. The reconstructed dMRI signal using this method shows a more structurally coherent representation of the data. Noise in gradient images is removed and the fiber orientation distribution functions show a smooth change in direction along a fiber tract. We showcase how the reconstruction can be used to calculate mean diffusivity, fractional anisotropy, and total apparent fiber density. These results can be achieved with a single model architecture, tuning only one hyperparameter. In this paper we also demonstrate how upsampling in both the angular and spatial domain yields reconstructions that are on par or better than existing methods.

Original languageEnglish
Title of host publicationComputational Diffusion MRI - 14th International Workshop, CDMRI 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsMuge Karaman, Remika Mito, Elizabeth Powell, Francois Rheault, Stefan Winzeck
Number of pages12
ISBN (Electronic)978-3-031-47292-3
ISBN (Print)9783031472916
Publication statusPublished - 7 Feb 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14328 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


This research was funded by the National Institute on Aging (NIA; U19AG066567). Data collection for this work was additionally supported, in part, by prior funding from the NIA grants U01AG006781 and RF1AG056326. All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the National Institute on Aging or the National Institutes of Health. We thank the participants of the Adult Changes in Thought (ACT) study for the data they have provided and the many ACT investigators and staff who steward that data. You can learn more about ACT at:

FundersFunder number
National Institutes of Health
National Institute on AgingU19AG066567, RF1AG056326, U01AG006781


    • Diffusion MRI
    • Implicit Neural Representation
    • Spherical Harmonics


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