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Abstract #1157

Coil to Coil: Self-supervised denoising using phased-array coil images

Juhyung Park1, Dongwon Park2, Hyeong-Geol Shin1, Eun-Jung Choi1, Dongmyung Shin1, Se Yong Chun1, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of


A self-supervised learning framework, Coil to Coil (C2C), is proposed. This method generates two noise-corrupted images from single phased-array coil data to train a denoising network and, therefore, requires no clean image nor acquisition of a pair of noisy images. The two images are processed to have the same signals and independent noises, satisfying conditions for the noise to noise algorithm, which requires paired noise-corrupted images. C2C shows the best performance among popular self-supervised denoising methods in both real and synthetic noised images, revealing little or no structure in the noise map.

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