Abstract #3610

Calibrationless SENSE Reconstruction with Deep Coil Sensitivity Learning

Chengyan Wang¹, Yan Li², Jun Lv³, Bo Li⁴, Fei Dai⁵, Weibo Chen⁶, and He Wang^1,5

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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