Muheng Li1, Jie Xiang2, Fuyixue Wang3,4, Zijing Dong3,5, and Kui Ying2
1Department of Automation, Tsinghua University, Beijing, China, 2Department of Engineering Phycics, Tsinghua University, Beijing, China, 3A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, 5Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States
1Department of Automation, Tsinghua University, Beijing, China, 2Department of Engineering Phycics, Tsinghua University, Beijing, China, 3A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, 5Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States
Through image reconstruction tests on human brain data set acquired by EPTI, we demonstrated the high efficiency of the kernel-based reconstruction with neural network by shortening the reconstruction time of 216×216×48×32 k-data from over 10 minutes to about 20 seconds.
Figure2. Process of restoring the missing k-data in the specified kernel. Extract the acquired data in this kernel and all the data in the target region as a 1D vector respectively. The mapping function can be fitted based on the fully sampled calibration data.
Figure4. Reconstructed images with different (a)loss function: MSE, MAE, Huber (b)number of nodes in each hidden layer (c)multi-contrast and reference images by conventional linear algorithm.