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

Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data.

Optica 9, 32-41 (2022)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
Deep learning (DL) shows promise for quantitating anatomical features and functional parameters of tissues in quantitative optoacoustic tomography (QOAT), but its application to deep tissue is hindered by a lack of ground truth data. We propose DL-based "QOAT-Net,"which functions without labeled experimental data: A dual-path convolutional network estimates absorption coefficients after training with data-label pairs generated via unsupervised "simulation-to-experiment"data translation. In simulations, phantoms, and ex vivo and in vivo tissues, QOAT-Net affords quantitative absorption images with high spatial resolution. This approach makes DL-based QOAT and other imaging applications feasible in the absence of ground truth data.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Photoacoustic Image-reconstruction; Optical-properties; Small-animals; Distributions; Absorption; Scattering; Media; Model
ISSN (print) / ISBN 2334-2536
Journal Optica
Quellenangaben Volume: 9, Issue: 1, Pages: 32-41 Article Number: , Supplement: ,
Publisher Optical Society of America (OSA)
Publishing Place Washington, DC
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Biological and Medical Imaging (IBMI)
Grants European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme
Key Fund of Shenzhen Natural Science Foundation
Natural Science Foundation of Tianjin Municipal Science and Technology Commission
National Natural Science Foundation of China