A Faithful Deep Sensitivity Estimation Makes High-quality MRI Reconstruction

Recent deep learning is superior in providing high-quality images and ultra-fast reconstructions in accelerated magnetic resonance imaging (MRI). Faithful coil sensitivity estimation is vital for MRI reconstruction. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network (JDSI). During the image artifacts removal, it gradually provides more faithful sensitivity maps, leading to greatly improved image reconstructions. Results on in vivo datasets and radiologist reader study demonstrate that, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the accelerated factor is high. Besides, JDSI also owns nice robustness to abnormal subjects.