Abstract #3426

EPI Phase correction using model-based deep learning reconstruction

Lili Wang¹, Fanwen Wang¹, Yinghua Chu², Xucheng Yu¹, Chengyan Wang³, and He Wang^1,3

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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