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Abstract #3610

Calibrationless SENSE Reconstruction with Deep Coil Sensitivity Learning

Chengyan Wang1, Yan Li2, Jun Lv3, Bo Li4, Fei Dai5, Weibo Chen6, and He Wang1,5
1Human Phenome Institute, Fudan University, Shanghai, China, 2Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Department of Computer Science, Yantai University, Yantai, China, 4The Third Affiliated Hospital of Nanchang University, Nanchang, China, 5Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 6Philips Healthcare, Shanghai, China

Conventional SENSE requires accurate estimation of coil sensitivity maps, which remains to be a challenge in practical scenarios. This study aims to apply CNN to extract coil sensitivity information from the undersampled center k-space, and use the estimated sensitivity maps for parallel imaging. Results show that no obvious residual signal can be seen in the reconstructed images for all cases, which indicates the efficacy of the proposed method. Besides, the CNN based SENSE image without ACS appears to be less noisy than conventional SENSE results with ACS, which may benefit from the denoising effect from CNN on the sensitivity maps.

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