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Imaging complex objects using learning tomography
Author(s): JooWon Lim; Alexandre Goy; Morteza Hasani Shoreh ; Michael Unser; Demetri Psaltis
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Paper 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.

Paper Details

Date Published: 23 February 2018
PDF: 6 pages
Proc. SPIE 10503, Quantitative Phase Imaging IV, 105031T (23 February 2018); doi: 10.1117/12.2288920
Show Author Affiliations
JooWon Lim, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Alexandre Goy, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Morteza Hasani Shoreh , Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Michael Unser, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Demetri Psaltis, Ecole Polytechnique Fédérale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 10503:
Quantitative Phase Imaging IV
Gabriel Popescu; YongKeun Park, Editor(s)

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