Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network
This work addresses the practical limitation of HARDI requiring many measurements, potentially enabling broader clinical use, though it appears incremental as it builds on existing compressed sensing and neural network methods.
The paper tackles the problem of reconstructing high angular resolution diffusion imaging (HARDI) from fewer measurements by using a 1D encoder-decoder convolutional neural network guided by compressed sensing theory, achieving accurate and fast reconstruction results.
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.