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Accelerated MRI shortens acquisition time by subsampling in the measurement$\kappa$-space. Recovering a high-fidelity anatomical image from subsampledmeasurements requires close cooperation between two components: (1) a samplerthat chooses the subsampling pattern and (2) a reconstructor that recoversimages from incomplete measurements. In this paper, we leverage the sequentialnature of MRI measurements, and propose a fully differentiable framework thatjointly learns a sequential sampling policy simultaneously with areconstruction strategy. This co-designed framework is able to adapt duringacquisition in order to capture the most informative measurements for aparticular target. Experimental results on the fastMRI knee dataset demonstratethat the proposed approach successfully utilizes intermediate informationduring the sampling process to boost reconstruction performance. In particular,our proposed method can outperform the current state-of-the-art learned$\kappa$-space sampling baseline on over 96% of test samples. We alsoinvestigate the individual and collective benefits of the sequential samplingand co-design strategies.

End-to-End Sequential Sampling and Reconstruction for MRI