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Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network (1903.09272v1)

Published 21 Mar 2019 in cs.CV

Abstract: 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.

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Authors (4)
  1. Shi Yin (28 papers)
  2. Zhengqiang Zhang (19 papers)
  3. Qinmu Peng (28 papers)
  4. Xinge You (50 papers)
Citations (8)

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