In this abstract, we introduce a novel pipeline for MRI reconstruction, which actively selects sampling trajectories in order to ensure high fidelity images, while speeding up the acquisition time. The pipeline is based on recent advances in deep learning and is composed of two networks interacting with each other in order to perform active acquisition. Results on a large scale knee dataset highlight the potential of the method when compared to standard acquisition heuristics. Moreover, we show that the learnt acquisition strategy efficiently reduces the reconstruction uncertainty and paves the way towards more applicable solutions for accelerating MRI.
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