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Refractive index tomography with a physics-based optical neural network.

Delong Yang1, Shaohui Zhang1,2,3, Chuanjian Zheng1

  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

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|November 29, 2023
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Summary
This summary is machine-generated.

This study introduces an untrained multi-slice neural network (MSNN) for refractive index (RI) tomography, improving imaging efficiency and clarity for biological samples. The novel MSNN addresses challenges in complex scattering scenarios, offering a new paradigm for RI tomography.

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Area of Science:

  • Biomedical Imaging
  • Optical Physics
  • Computational Biology

Background:

  • Three-dimensional refractive index (RI) tomography is valuable in life sciences but struggles with complex scattering samples.
  • Conventional algorithms face challenges in global optimization, leading to slow and ineffective reconstructions.

Purpose of the Study:

  • To develop an efficient and robust method for RI tomography that overcomes limitations of conventional algorithms.
  • To introduce an untrained neural network with physical interpretability for complex scattering scenarios.

Main Methods:

  • Proposed an untrained multi-slice neural network (MSNN) with layers corresponding to beam propagation.
  • Integrated a 'scattering attenuation layer' to account for multiple backscattering effects.
  • Utilized optimization of intensity images for network recovery and learnable parameters for illumination calibration.

Main Results:

  • The MSNN demonstrated effectiveness and feasibility through simulations and experiments.
  • Achieved enhanced clarity and increased efficiency in RI tomography.
  • The network requires no pre-training and exhibits good generalization.

Conclusions:

  • The proposed MSNN offers a novel and effective paradigm for RI tomography, particularly for samples deviating from weak scattering approximations.
  • MSNN enhances imaging performance by addressing complex light propagation and scattering effects.
  • This approach provides a powerful tool for advanced biological imaging applications.