PuSH - Publication Server of Helmholtz Zentrum München: LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

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)

DOI

PMC

Open Access Green as soon as Postprint is submitted to ZB.

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Biomedical Applications ; Deep Learning ; High Quality ; Limited‐ ; View ; Optoacoustic Imaging; Tomography

ISSN (print) / ISBN 1864-063X

e-ISSN 1864-0648

Journal Journal of Biophotonics

Quellenangaben Volume: 14, Issue: 2, Pages: e202000325

Publisher Wiley

Publishing Place Postfach 101161, 69451 Weinheim, Germany

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Biological and Medical Imaging (IBMI)

Grants Tianjin Municipal Government of China
National Natural Science Foundation of China