phase correction of magnetic resonance spectra based on convolutional neural network
Qingjia Bao1, Piqiang Li2, Zhao Li1, Kewen Liu2, Chongxin Bai2, Peng Sun3, Jiazheng Wang3, Jie Wang1, Feng Pan1, Weida Xie2, Lian Yang4, and Chaoyang Liu1
1State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Phys, Wuhan, China, 2Wuhan University of Technology School of Information Engineering, Wuhan, China, 3Philips Healthcare, Beijing, China, 4Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
We proposed a spectrum-to-spectrum/spectrum-to-phase phase correction method based on a neural network for magnetic resonance spectra. The former network obtains phase-corrected spectra by the end-to-end training the mapping between the manually corrected spectra and uncorrected spectra. And the latter can achieve more accurate phase correction by predicting the zero- and first-order phases for correction. The result shows that the proposed network can effectively obtain high-quality phase correction spectra even under noisy and baseline distortion conditions.
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