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.
Highlights
Infant brain development involves complex cerebral growth and maturation with the white matter (WM) undergoing rapid myelination and synaptogenesis (Qiu et al, 2015)
Quantitative Comparison – Synthetic Data Using the fractional anisotropy (FA) image of noise-free high angular resolution (HAR) data as ground truth, we evaluated the quality of the upsampled data using MNAD and Peak signal-to-noise ratio (PSNR)
The close-up views of RMSE maps, shown in the bottom row of Figure 4, indicate that our method gives lower RMSE than spherical radial basis functions (SRBFs) interpolation and spherical harmonics (SHs) interpolation, which demonstrates that the upsampled data given by our method is closer to ground truth
Summary
Infant brain development involves complex cerebral growth and maturation with the white matter (WM) undergoing rapid myelination and synaptogenesis (Qiu et al, 2015). Diffusion MRI (DMRI) has been widely employed to study this developmental process in vivo (Yap et al, 2011; Huang et al, 2013; Dubois et al, 2014; Qiu et al, 2015). Mean diffusivity (Dubois et al, 2014; Qiu et al, 2015) and structural connectivity (Yap et al, 2011; Huang et al, 2013) have been used to study early brain development. The angular resolution of DMRI data is determined by the number of gradients used in data acquisition. A larger number of DW images allows the use of advanced diffusion models but prolong the acquisition time, which is prohibitive in clinical settings
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