Eric Z. Chen1, Yongquan Ye2, Xiao Chen1, Jingyuan Lyu2, Zhongqi Zhang3, Yichen Hu2, Terrence Chen1, Jian Xu2, and Shanhui Sun1
1United Imaging Intelligence, Cambridge, MA, United States, 2UIH America, Inc., Houston, TX, United States, 3United Imaging Healthcare, Shanghai, China
1United Imaging Intelligence, Cambridge, MA, United States, 2UIH America, Inc., Houston, TX, United States, 3United Imaging Healthcare, Shanghai, China
This is the first work applying the deep learning approach to the 3D MULTIPLEX MRI reconstruction. The proposed method shows good performance in image quality and reconstruction time.
Figure 1. The network architecture for 3D MULTIPLEX data reconstruction. The model includes five convolutional blocks and each block contains five 3D convolutional layers and one data consistency layer. The feature maps are 48 for the first four 3D convolutional layers and 2 for the last 3D convolutional layer in each block. The x, y indicate image and kspace, respectively. The real and imaginary numbers of complex values are transformed into two channels and fed into the network.
Figure 2. Examples of reconstructed MULTIPLEX echo images by the proposed deep learning method at 3X and 5X accelerations. FA1 and FA2 indicate two different flip angle configurations. Three (Echo1, Echo4 and Echo7) out of seven echo configures are showed due to space constraints. All errors are multiplied by 50 for better visualization. The same 2D axial slice from each 3D image is plotted.
