End-to-End Sequential Sampling and Reconstruction for MR Imaging

Tianwei Yin*,2    Zihui Wu*,1    He Sun1    Adrian V. Dalca3,4
Yisong Yue1    Katherine L. Bouman1

1California Institute of Technology     2University of Texas at Austin     3Harvard Medical School     4Massachusetts Institute of Technology     *Equal contribution


Accelerated MRI shortens acquisition time by subsampling in the measurement k-space. Recovering a high-fidelity anatomical image from subsampled measurements requires close cooperation between two components: (1) a sampler that chooses the subsampling pattern and (2) a reconstructor that recovers images from incomplete measurements. In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy. This co-designed framework is able to adapt during acquisition in order to capture the most informative measurements for a particular target (see the figure above). Experimental results on the fastMRI knee dataset demonstrate that the proposed approach successfully utilizes intermediate information during the sampling process to boost reconstruction performance. In particular, our proposed method outperforms the current state-of-the-art learned k-space sampling baseline on up to 96.96% of test samples. We also investigate the individual and collective benefits of the sequential sampling and co-design strategies.

paper [arXiv] code [Github] supplement [pdf] poster [pdf]


Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, and Katherine L. Bouman (2021). "End-to-End Sequential Sampling and Reconstruction for MR Imaging." arXiv:2105.06460.