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 over 96% of test samples. We also investigate the individual and collective benefits of the sequential sampling and co-design strategies.
paper [link] code [Github] 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 MRI." Machine Learning for Health (ML4H) 2021. (Best Paper Award)
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