High-fidelity optical diffraction tomography of multiple scattering samples

Joowon Lim; Ahmed B. Ayoub; Elizabeth E. Antoine; Demetri Psaltis

doi:10.1038/s41377-019-0195-1

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High-fidelity optical diffraction tomography of multiple scattering samples

Article

- High-fidelity optical diffraction tomography of multiple scattering samples
- Light: Science & Applications Vol. 8, Issue 5, Pages: 807-818(2019)
- Affiliations：
  
  Ecole Polytechnique Fédérale de Lausanne, Optics Laboratory, CH-1015 Lausanne, Switzerland
- Author bio：
  
  Joowon Lim (joowon.lim@epfl.ch)
- Funds：
- DOI：10.1038/s41377-019-0195-1
  CLC：
- Published：2019，
  
  Published Online：11 September 2019，
  
  Received：12 February 2019，
  
  Revised：14 August 2019，
  
  Accepted：19 August 2019
- Accepted：
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Lim, J. et al. High-fidelity optical diffraction tomography of multiple scattering samples. Light: Science & Applications, 8, 807-818 (2019).
DOI：

Lim, J. et al. High-fidelity optical diffraction tomography of multiple scattering samples. Light: Science & Applications, 8, 807-818 (2019). DOI： 10.1038/s41377-019-0195-1.

摘要

Abstract

We propose an iterative reconstruction scheme for optical diffraction tomography that exploits the split-step non-paraxial (SSNP) method as the forward model in a learning tomography scheme. Compared with the beam propagation method (BPM) previously used in learning tomography (LT-BPM)

the improved accuracy of SSNP maximizes the information retrieved from measurements

relying less on prior assumptions about the sample. A rigorous evaluation of learning tomography based on SSNP (LT-SSNP) using both synthetic and experimental measurements confirms its superior performance compared with that of the LT-BPM. Benefiting from the accuracy of SSNP

LT-SSNP can clearly resolve structures that are highly distorted in the LT-BPM. A serious limitation for quantifying the reconstruction accuracy for biological samples is that the ground truth is unknown. To overcome this limitation

we describe a novel method that allows us to compare the performances of different reconstruction schemes by using the discrete dipole approximation to generate synthetic measurements. Finally

we explore the capacity of learning approaches to enable data compression by reducing the number of scanning angles

which is of particular interest in minimizing the measurement time.

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Keywords

references

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