Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data
Li J, Wang C, Chen T, Lu T, Li S, Sun B, Gao F, Ntziachristos V (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 9
Pages Range: 32-41
Journal Issue: 1
Abstract
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.
Involved external institutions
Tianjin University (TJU) / 天津大学
China (CN)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
China (CN)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
How to cite
APA:
Li, J., Wang, C., Chen, T., Lu, T., Li, S., Sun, B.,... Ntziachristos, V. (2022). Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data. Optica, 9(1), 32-41. https://doi.org/10.1364/OPTICA.438502
MLA:
Li, Jiao, et al. "Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data." Optica 9.1 (2022): 32-41.
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