Title: Fiber Orientation Estimation Guided by a Deep Network,
Abstract: Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction has been used to estimate FOs with a relatively small number of
diffusion gradients. However, accurate FO estimation in regions with complex FO
configurations in the presence of noise can still be challenging. In this work
we explore the use of a deep network for FO estimation in a dictionary-based
framework and propose an algorithm named Fiber Orientation Reconstruction
guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
smaller dictionary encoding coarse basis FOs to represent the diffusion
signals. To estimate the mixture fractions of the dictionary atoms (and thus
coarse FOs), a deep network is designed specifically for solving the sparse
reconstruction problem. Here, the smaller dictionary is used to reduce the
computational cost of training. Second, the coarse FOs inform the final FO
estimation, where a larger dictionary encoding dense basis FOs is used and a
weighted l1-norm regularized least squares problem is solved to encourage FOs
that are consistent with the network output. FORDN was evaluated and compared
with state-of-the-art algorithms that estimate FOs using sparse reconstruction
on simulated and real dMRI data, and the results demonstrate the benefit of
using a deep network for FO estimation.