Abstract #1753

MAGnitude Image to Complex (MAGIC)-Net: reconstructing multi-coil complex images with pre-trained network using synthesized raw data

Fanwen Wang¹, Hui Zhang¹, Jiawei Han¹, Fei Dai¹, Yuxiang Dai¹, Weibo Chen², Chengyan Wang³, and He Wang^1,3

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Philips Healthcare, Shanghai, China, ³Human Phenome Institute, Fudan University, Shanghai, China

This study proposed a novel MAGnitude Image to Complex (MAGIC) Network to reconstruct images using deep learning with limited number of training data. Collecting complex multi-coil data is inconvenient since it is beyond the routine examination. However, there are many magnitude images available in hospitals. By applying deformation between the magnitude image and complex image, MAGIC Net succeeded in synthesizing deformed data for training and enabled deep learning methods. Results show that with the same original data, MAGIC-Net outperforms the conventional CG-SENSE in PSNR for all undersampling trajectories with high resolution b = 0 and b = 1000 s/mm².

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