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arXiv · 2304.14188

Nonparametric Modeling of Diffusion MRI Signal in Q-space

Arkaprava Roy·Zhou Lan·Zhengwu Zhang
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Abstract

This paper describes a novel nonparametric model for modeling diffusion MRI signals in q-space. In q-space, diffusion MRI signal is measured for a sequence of magnetic strengths (b-values) and magnetic gradient directions (b-vectors). We propose a Poly-RBF model, which employs a bidirectional framework with polynomial bases to model the signal along the b-value direction and Gaussian radial bases across the b-vectors. The model can accommodate sparse data on b-values and moderately dense data on b-vectors. The utility of Poly-RBF is inspected for two applications: 1) prediction of the dMRI signal, and 2) harmonization of dMRI data collected under different acquisition protocols with different scanners. Our results indicate that the proposed Poly-RBF model can more accurately predict the unmeasured diffusion signal than its competitors such as the Gaussian process model in {\tt Eddy} of FSL. Applying it to harmonizing the diffusion signal can significantly improve the reproducibility of derived white matter microstructure measures.

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Arkaprava Roy, Zhou Lan, Zhengwu Zhang. 2023-04-27. Nonparametric Modeling of Diffusion MRI Signal in Q-space. https://arxiv.org/abs/2304.14188

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