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

Synopsis

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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