  High angular resolution diffusion imaging (HARDI) demands a lager amount of
data measurements compared to diffusion tensor imaging, restricting its use in
practice. In this work, we explore a learning-based approach to reconstruct
HARDI from a smaller number of measurements in q-space. The approach aims to
directly learn the mapping relationship between the measured and HARDI signals
from the collecting HARDI acquisitions of other subjects. Specifically, the
mapping is represented as a 1D encoder-decoder convolutional neural network
under the guidance of the compressed sensing (CS) theory for HARDI
reconstruction. The proposed network architecture mainly consists of two parts:
an encoder network produces the sparse coefficients and a decoder network
yields a reconstruction result. Experiment results demonstrate we can robustly
reconstruct HARDI signals with the accurate results and fast speed.
