Deep Inception Residual Network (DIRN) for Reconstruction of Undersampled Brain MR Image
Sekeun Kim1, Yeonggul Jang2, Hackjoon Shim3, and Hyukjae Chang4
1Graduate program in Biomedical Engineering The Graduate School, Yonsei University, Seoul, Korea, Republic of, 2Brain Korea 21 PLUS Project for Medical Science, Yonsei University, seoul, Korea, Republic of, 3Yonsei-Cedars-Sinai Integrative Cardiac Imaging Research Center, seoul, Korea, Republic of, 4Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University, College of Medicine, seoul, Korea, Republic of
Acquiring the full-sampling k-space magnetic resonance imaging (MRI) data for detailed anatomical information is ideal. We propose the Deep Inception Residual Network (DIRN) based on a deep convolutional neural network (DCNN) consisting of inception blocks and residual blocks for the reconstruction of the MR image from undersampled k-space data. The experimental results on an ADNI dataset demonstrate that DIRN is appropriate for a reconstruction of brain MR images.
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