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Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffersfrom prolonged scan time. To alleviate this limitation, advanced fast MRItechnology attracts extensive research interests. Recent deep learning hasshown its great potential in improving image quality and reconstruction speed.Faithful coil sensitivity estimation is vital for MRI reconstruction. However,most deep learning methods still rely on pre-estimated sensitivity maps andignore their inaccuracy, resulting in the significant quality degradation ofreconstructed images. In this work, we propose a Joint Deep Sensitivityestimation and Image reconstruction network, called JDSI. During the imageartifacts removal, it gradually provides more faithful sensitivity maps withhigh-frequency information, leading to improved image reconstructions. Tounderstand the behavior of the network, the mutual promotion of sensitivityestimation and image reconstruction is revealed through the visualization ofnetwork intermediate results. Results on in vivo datasets and radiologistreader study demonstrate that, for both calibration-based and calibrationlessreconstruction, the proposed JDSI achieves the state-of-the-art performancevisually and quantitatively, especially when the acceleration factor is high.Additionally, JDSI owns nice robustness to patients and autocalibrationsignals.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging