Learning approach to optical tomography

By Morteza Hasani Shoreh, Ulugbek S. Kamilov, Ioannis N. Papadopoulos, Alexandre Goy, Cédric Vonesch, Michael A. Unser, Demetri Psaltis. 2015

320 citations recorded by the source corpus.

Optical tomography has been widely investigated for biomedical imaging applications. In recent years optical tomography has been combined with digital holography and has been employed to produce high-quality images of phase objects such as cells. In this paper we describe a method for imaging 3D phase objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of Hela cells.

Subfields: Atomic and Molecular Physics, and Optics, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/54be79bf266c34f5833a02dc73a92459ff99237e

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In Atomic and Molecular Physics, and Optics

In Biomedical Engineering

Citations in the corpus

Flash Photography for Data-Driven Hidden Scene Recovery (2018, methodology): “Data-driven techniques have also been applied to problems in the computational imaging community such as phase imaging [34], compressive imaging [25], tomography [22], microscopy [18], imaging [17], and imaging through scattering media [31].”
Machine learning and applications in ultrafast photonics (2020, background): “Recently, deep neural networks have found applications in solving such inverse problems in 189 areas such as coherent imaging [49, 50], imaging through scattering media [51, 52] or super190 resolution [53], and they are now also showing great promise in pulse reconstruction.”
Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “Accordingly, there has been a surge of interest in applying deep learning to microscopy, producing new techniques to both reconstruct images and design a microscope’s illumination patterns and optical elements [34]–[40].”