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

Lu T, Chen T, Gao F, Sun B, Ntziachristos V, Li J (2021)

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Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 14

Article Number: e202000325

Journal Issue: 2

DOI: 10.1002/jbio.202000325

Abstract

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.

Involved external institutions

How to cite

APA:

Lu, T., Chen, T., Gao, F., Sun, B., Ntziachristos, V., & Li, J. (2021). LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets. Journal of Biophotonics, 14(2). https://doi.org/10.1002/jbio.202000325

MLA:

Lu, Tong, et al. "LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets." Journal of Biophotonics 14.2 (2021).

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