Yang, FangshuPham, Thanh-AnGupta, HarshitUnser, MichaelMa, Jianwei2020-04-102020-04-102020-04-102020-02-0310.1364/OE.381413https://infoscience.epfl.ch/handle/20.500.14299/168088WOS:000514570800102Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been derived by the minimization of an objective function in which the data-fidelity term encourages measurement consistency while the regularization term enforces prior constraints. In this work, we propose to train a convolutional neural network (CNN) as the projector in a projected-gradient-descent method. We iteratively produce high-quality estimates and ensure measurement consistency, thus keeping the best of CNN-based and regularization-based worlds. Our experiments on two-dimensional-simulated and real data show an improvement over other conventional or deep-learning-based methods. Furthermore, our trained CNN projector is general enough to accommodate various forward models for the handling of multiple-scattering events. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing AgreementOpticsOpticsrefractive-indeximage-reconstructionmultiple-scatteringinverse problemsneural-networkalgorithmsefficientcellstext::journal::journal article::research article