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Abstract #3426

EPI Phase correction using model-based deep learning reconstruction

Lili Wang1, Fanwen Wang1, Yinghua Chu2, Xucheng Yu1, Chengyan Wang3, and He Wang1,3
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2MR Collaboration, Siemens Healthcare Ltd., Shanghai, China, 3Human Phenome Institute, Fudan University, Shanghai, China

The commonly used approach of Nyquist ghost correction in echo planar imaging (EPI) include linear phase correction and model-free 2D phase correction. The recent proposed method termed ‘PEC-SENSE’ incorporates 2D phase error correction with parallel imaging can robustly eliminate Nyquist ghost for EPI data,while does not act well when a distortion mismatch exsisted between the calibration data and image data. The proposed model-based deep learning method can obtain more robust phase maps than PEC-SENSE to remove image ghost and preserve the image SNR in low or high-accelerated EPI data.

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