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. (C) 2022 Optical Society of America under the terms of the OSA Open Access Publishing Agreement     «

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 sim...     »