Nonparametric Modeling of Diffusion MRI Signal in Q-Space

Abstract

This paper describes a novel nonparametric model for modeling diffusion MRI signals in q-space. In q-space, the diffusion MRI signal is measured for a sequence of diffusion sensitivities (b-values) and diffusion 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 the FSL’s Eddy tool. Applying it to harmonizing the diffusion signal can significantly improve the reproducibility of derived white matter microstructure measures.

Keywords

• Diffusion MRI
• dMRI data standardization
• q-space modeling
• white matter
• reproducibility