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.

翻译：磁共振成像（MRI）是一种重要的诊断工具，但存在扫描时间过长的问题。为克服这一局限，先进的快速磁共振成像技术吸引了广泛的研究兴趣。近年来，深度学习在提升图像质量和重建速度方面展现出巨大潜力。可靠的线圈灵敏度估计对于MRI重建至关重要。然而，多数深度学习方法仍依赖预估计的灵敏度图，并忽略其不准确性，导致重建图像质量显著下降。本文提出一种联合深度灵敏度估计与图像重建网络（JDSI）。在图像伪影去除过程中，该网络逐步提供包含高频信息的更忠实灵敏度图，从而改善图像重建质量。为理解网络行为，通过可视化网络中间结果揭示了灵敏度估计与图像重建的相互促进关系。基于活体数据集和放射科医师读片研究的实验结果表明，在基于校准和无校准重建中，所提出的JDSI方法在视觉和定量指标上均达到当前最优性能，尤其在加速因子较高时表现突出。此外，JDSI对患者差异及自动校准信号具有良好的鲁棒性。