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.
@article{arxiv.1903.09272,
title = {Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network},
author = {Shi Yin and Zhengqiang Zhang and Qinmu Peng and Xinge You},
journal= {arXiv preprint arXiv:1903.09272},
year = {2019}
}