  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.
