  Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers
from prolonged scan time. To alleviate this limitation, advanced fast MRI
technology attracts extensive research interests. Recent deep learning has
shown 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 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 with
high-frequency information, leading to 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, for both calibration-based and calibrationless
reconstruction, the proposed JDSI achieves the state-of-the-art performance
visually and quantitatively, especially when the acceleration factor is high.
Additionally, JDSI owns nice robustness to patients and autocalibration
signals.
