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. However,
most deep learning methods still rely on pre-estimated sensitivity maps and
ignore their inaccuracy, resulting in the significant quality degradation of
reconstructed images. In this work, we propose a Joint Deep Sensitivity
estimation and Image reconstruction network, called JDSI. During the image
artifacts removal, it gradually provides more faithful sensitivity maps,
leading to greatly improved image reconstructions. To understand the behavior
of the network, the mutual promotion of sensitivity estimation and image
reconstruction is revealed through the visualization of network intermediate
results. 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. Additionally,
JDSI owns nice robustness to abnormal subjects and different number of
autocalibration signals.

本文提出了一种名为 JDSI 的联合深度灵敏度和图像重建网络，能够逐步提供更准确的灵敏度图，从而显着提高了重建图像的质量和状态，特别是在加速系数高的情况下，具有很好的鲁棒性和性能表现。