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Abstract #4707

Development of a deep learning method for phase unwrapping MR images

Kanghyun Ryu1, Sung-Min Gho2, Yoonho Nam3, Kevin Koch4, and Dong-Hyun Kim1

1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2MR Clinical research and Development, GE Healthcare, Seoul, Korea, Republic of, 3Seoul St.Mary's Hospital, The Catholic University of korea, Seoul, Korea, Republic of, 4Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States

MRI phase images are increasingly used for susceptibility mapping and distortion correction in function and diffusion MRI. However, acquired values of phase maps are wrapped between [-π π ] and require an additional phase unwrapping process. Here we developed a novel deep learning method that can learn the transformation between the wrapped phase images and the corresponding unwrapped phase images. The method was tested for numerical simulations and on actual MR images.

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