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Lu, T.* ; Chen, T.* ; Gao, F.* ; Sun, B.* ; Ntziachristos, V. ; Li, J.*

LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets.

J. Biophotonics 14, e202000325 (2021)
Open Access Green as soon as Postprint is submitted to ZB.
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 degrees. 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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Biomedical Applications ; Deep Learning ; High Quality ; Limited‐ ; View ; Optoacoustic Imaging; Tomography
ISSN (print) / ISBN 1864-063X
e-ISSN 1864-0648
Quellenangaben Volume: 14, Issue: 2, Pages: e202000325 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place Postfach 101161, 69451 Weinheim, Germany
Reviewing status Peer reviewed
Grants Tianjin Municipal Government of China
National Natural Science Foundation of China