基于自监督网络的肝脏磁共振R2*参数图像重建
陆琪琪,连梓锋,李嘉龙,斯文彬,麦兆华,冯衍秋*(
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Magnetic Resonance R2* Parameter Mapping of Liver Based on Self-supervised Deep Neural Network
LU Qiqi,LIAN Zifeng,LI Jialong,SI Wenbin,MAI Zhaohua,FENG Yanqiu*()
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图2. 卷积神经网络的结构. 网络的输入为不同TE时间采集得到的12幅肝脏$T_{2}^{*}$加权图像，输出为2幅参数图像，分别对应着${{S}_{0}}$和$R_{2}^{*}$参数图像
Fig. 2. The architecture of the convolutional neural network used in the proposed method. The inputs of the network are 12$T_{2}^{*}$-weighted images acquired at different TEs, and the outputs are 2 parameter maps corresponding to${{S}_{0}}$and$R_{2}^{*}$maps, respectively
