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

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