The network we propose in this work (xSDNet) jointly reconstructs and segments cardiac functional MR images which were sampled below the Nyquist rate. The model is based on disentangled representation learning and factorizes images into spatial factors and a modality vector. The achieved image quality and the fidelity of the delivered segmentation masks promise a considerable acceleration of both acquisition and data processing.
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