Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
This method addresses the challenge of reducing acquisition time in diffusion MRI for medical imaging applications, offering a potential replacement for standard DSI in time-limited scenarios.
The authors tackled the problem of reconstructing diffusion spectrum imaging (DSI) from non-uniform and undersampled diffusion MRI data by using Gaussian process regression to estimate signals at arbitrary q-space locations, enabling synthetic DSI with minimal accuracy loss and superior performance compared to linear interpolation.
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on non-uniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.