Wang Zi, Fang Haoming, Qian Chen, Shi Boxuan, Bao Lijun, Zhu Liuhong, Zhou Jianjun, Wei Wenping, Lin Jianzhong, Guo Di, Qu Xiaobo
IEEE J Biomed Health Inform. 2024 Feb 5;PP. doi: 10.1109/JBHI.2024.3360128.
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.
磁共振成像（MRI）是一种重要的诊断工具，但存在扫描时间长的问题。为了缓解这一限制，先进的快速MRI技术引起了广泛的研究兴趣。近年来，深度学习在提高图像质量和重建速度方面显示出巨大潜力。准确的线圈灵敏度估计对于MRI重建至关重要。然而，大多数深度学习方法仍然依赖预先估计的灵敏度图，而忽略了其不准确性，导致重建图像质量显著下降。在这项工作中，我们提出了一种联合深度灵敏度估计和图像重建网络，称为JDSI。在去除图像伪影的过程中，它逐渐提供具有高频信息的更准确的灵敏度图，从而改善图像重建。为了理解网络的行为，通过可视化网络中间结果揭示了灵敏度估计和图像重建的相互促进作用。体内数据集和放射科医生阅片研究的结果表明，对于基于校准和无校准的重建，所提出的JDSI在视觉和定量方面都达到了当前的最佳性能，特别是当加速因子较高时。此外，JDSI对患者和自动校准信号具有良好的鲁棒性。
