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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)

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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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