Open AccessSelf-supervised PET Denoising
Author(s) -
Si Young Yie,
Seung Kwan Kang,
Donghwi Hwang,
Jae Sung Lee
Publication year - 2020
Publication title -
nuclear medicine and molecular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.534
H-Index - 23
eISSN - 1869-3482
pISSN - 1869-3474
DOI - 10.1007/s13139-020-00667-2
Subject(s) - noise reduction , artificial intelligence , noise (video) , computer science , pattern recognition (psychology) , test data , image (mathematics) , programming language
Early deep-learning-based image denoising techniques mainly focused on a fully supervised model that learns how to generate a clean image from the noisy input (noise2clean: N2C). The aim of this study is to explore the feasibility of the self-supervised methods (noise2noise: N2N and noiser2noise: Nr2N) for PET image denoising based on the measured PET data sets by comparing their performance with the conventional N2C model.