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LV‐GAN : A deep learning approach for limited‐view optoacoustic imaging based on hybrid datasets
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 - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000325
Subject(s) - computer science , artifact (error) , artificial intelligence , generative adversarial network , deep learning , image quality , computer vision , quality (philosophy) , image (mathematics) , physics , quantum mechanics
The optoacoustic imaging (OAI) methods are rapidly evolving for resolving optical contrast in medical imaging applications. In practice, measurement strategies are commonly implemented under limited‐view conditions due to oversized image objectives or system design limitations. Data acquired by limited‐view detection may impart artifacts and distortions in reconstructed optoacoustic (OA) images. We propose a hybrid data‐driven deep learning approach based on generative adversarial network (GAN), termed as LV‐GAN, to efficiently recover high quality images from limited‐view OA images. Trained on both simulation and experiment data, LV‐GAN is found capable of achieving high recovery accuracy even under limited detection angles less than 60 ° . The feasibility of LV‐GAN for artifact removal in biological applications was validated by ex vivo experiments based on two different OAI systems, suggesting high potential of a ubiquitous use of LV‐GAN to optimize image quality or system design for different scanners and application scenarios.