Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

TL;DR

This paper introduces a novel q-space upsampling method for infant diffusion MRI that leverages neighborhood matching in x-q space to improve data quality and reduce scan time.

Contribution

The proposed framework uses neighborhood matching in x-q space to regularize high angular resolution diffusion MRI estimation, effectively utilizing white matter structure information.

Findings

Outperforms spherical basis function interpolation methods in quality.

Effective in synthetic and infant MRI data evaluations.

Produces higher quality high angular resolution diffusion data.

Abstract

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.