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.Comment: 12 pages, 6 figures, accepted for cdMRI workshop at MICCAI 202