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- W4385965745 abstract "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." @default.
- W4385965745 created "2023-08-18" @default.
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- W4385965745 date "2023-08-16" @default.
- W4385965745 modified "2023-09-26" @default.
- W4385965745 title "Neural Spherical Harmonics for structurally coherent continuous representation of diffusion MRI signal" @default.
- W4385965745 doi "https://doi.org/10.48550/arxiv.2308.08210" @default.
- W4385965745 hasPublicationYear "2023" @default.
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