Nonparametric Modeling of Diffusion MRI Signal in Q-space
arxiv(2023)
摘要
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 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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