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Magnetic resonance imaging (MRI) acquisition, reconstruction, andsegmentation are usually processed independently in the conventional practiceof MRI workflow. It is easy to notice that there are significant relevancesamong these tasks and this procedure artificially cuts off these potentialconnections, which may lead to losing clinically important information for thefinal diagnosis. To involve these potential relations for further performanceimprovement, a sequential multi-task joint learning network model is proposedto train a combined end-to-end pipeline in a differentiable way, aiming atexploring the mutual influence among those tasks simultaneously. Our designconsists of three cascaded modules: 1) deep sampling pattern learning moduleoptimizes the $k$-space sampling pattern with predetermined sampling rate; 2)deep reconstruction module is dedicated to reconstructing MR images from theundersampled data using the learned sampling pattern; 3) deep segmentationmodule encodes MR images reconstructed from the previous module to segment theinterested tissues. The proposed model retrieves the latently interactive andcyclic relations among those tasks, from which each task will be mutuallybeneficial. The proposed framework is verified on MRB dataset, which achievessuperior performance on other SOTA methods in terms of both reconstruction andsegmentation.

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline