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Author(s) -
Lu Tong,
Chen Tingting,
Gao Feng,
Sun Biao,
Ntziachristos Vasilis,
Li Jiao
Publication year - 2021
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202170005
Subject(s) - software portability , artifact (error) , computer science , cover (algebra) , image quality , artificial intelligence , ex vivo , computer vision , optoacoustic imaging , biomedical engineering , optics , image (mathematics) , medicine , in vivo , physics , engineering , biology , mechanical engineering , microbiology and biotechnology , programming language
In optoacoustic imaging methods, measurement strategies are commonly implemented under limited‐view conditions, leading to artifacts and distortions in reconstructed optoacoustic images. We propose a hybrid data‐driven deep learning approach, termed as LV‐GAN, to efficiently recover high quality images from limited‐view optoacoustic images. The feasibility of LV‐GAN for artifact removal in biological applications and the portability of LV‐GAN was validated by ex vivo experiments based on two different optoacoustic imaging systems. Further details can be found in the article by Tong Lu, Tingting Chen, Feng Gao, Biao Sun, Vasilis Ntziachristos, and Jiao Li ( e202000325 ).