  Due to recent technological advances, large brain imaging data sets can now
be collected. Such data are highly complex so extraction of meaningful
information from them remains challenging. Thus, there is an urgent need for
statistical procedures that are computationally scalable and can provide
accurate estimates that capture the neuronal structures and their
functionalities. We propose a fast method for estimating the fiber orientation
distribution(FOD) based on diffusion MRI data. This method models the observed
dMRI signal at any voxel as a convolved and noisy version of the underlying
FOD, and utilizes the spherical harmonics basis for representing the FOD, where
the spherical harmonic coefficients are adaptively and nonlinearly shrunk by
using a James-Stein type estimator. To further improve the estimation accuracy
by enhancing the localized peaks of the FOD, as a second step a
super-resolution sharpening process is then applied. The resulting estimated
FODs can be fed to a fiber tracking algorithm to reconstruct the white matter
fiber tracts. We illustrate the overall methodology using both synthetic data
and data from the Human Connectome Project.
