Chen Xinran, Wang Wei, Huang Jianpan, Wu Jian, Chen Lin, Cai Congbo, Cai Shuhui, Chen Zhong
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, Fujian, People's Republic of China.
Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, People's Republic of China.
Magn Reson Med. 2022 Jun;87(6):2811-2825. doi: 10.1002/mrm.29172. Epub 2022 Jan 31.
To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images.
Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods.
With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results.
Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-of-the-art methods.
提出一种基于深度学习的时空编码单激发磁共振成像（MRI）重建方法，以同时获取水和脂肪图像。
时空编码MRI是一种超快成像分支，因其特殊的二次相位调制能够编码化学位移信息。提出了一种使用二维U-Net的深度学习方法来重建时空编码信号并同时获取水和脂肪图像。U-Net的训练数据由MRiLab软件（版本1.3）通过各种合成模型生成。在7.0 T的瓦里安MRI系统（安捷伦科技公司，加利福尼亚州圣克拉拉）上对离体猪肉和活体大鼠进行了数值模拟和实验，并将深度学习结果与通过最先进算法获得的结果进行了比较。使用结构相似性指数和信鬼比来评估不同重建方法的残余伪影。
通过训练有素的神经网络，所提出的深度学习方法在个人计算机上能够在0.46 s内完成信号重建，这与共轭梯度法（0.41 s）相当，并且比最先进的超分辨水脂图像重建方法（30.31 s）快得多。数值模拟、离体猪肉实验和活体大鼠实验的结果表明，与其他两种方法相比，深度学习方法能够实现更好的保真度和更高的空间分辨率。信鬼比结果表明，深度学习方法在伪影抑制方面也具有很大优势。
与最先进的方法相比，基于深度学习的时空编码MRI能够提供具有更好性能的超快水脂分离。
