23 February 2018 Imaging complex objects using learning tomography
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Proceedings Volume 10503, Quantitative Phase Imaging IV; 105031T (2018) https://doi.org/10.1117/12.2288920
Event: SPIE BiOS, 2018, San Francisco, California, United States
Abstract
Optical diffraction tomography (ODT) can be described using the scattering process through an inhomogeneous media. An inherent nonlinearity exists relating the scattering medium and the scattered field due to multiple scattering. Multiple scattering is often assumed to be negligible in weakly scattering media. This assumption becomes invalid as the sample gets more complex resulting in distorted image reconstructions. This issue becomes very critical when we image a complex sample. Multiple scattering can be simulated using the beam propagation method (BPM) as the forward model of ODT combined with an iterative reconstruction scheme. The iterative error reduction scheme and the multi-layer structure of BPM are similar to neural networks. Therefore we refer to our imaging method as learning tomography (LT). To fairly assess the performance of LT in imaging complex samples, we compared LT with the conventional iterative linear scheme using Mie theory which provides the ground truth. We also demonstrate the capacity of LT to image complex samples using experimental data of a biological cell.
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JooWon Lim, JooWon Lim, Alexandre Goy, Alexandre Goy, Morteza Hasani Shoreh , Morteza Hasani Shoreh , Michael Unser, Michael Unser, Demetri Psaltis, Demetri Psaltis, } "Imaging complex objects using learning tomography", Proc. SPIE 10503, Quantitative Phase Imaging IV, 105031T (23 February 2018); doi: 10.1117/12.2288920; https://doi.org/10.1117/12.2288920
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