Abstract #4715

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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