Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Abstract

Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to thin samples with low refractive-index contrasts. More recent works have shown the benefit of adopting nonlinear models. They account for multiple scattering and reflections, improving the quality of reconstruction. To reduce the complexity and memory requirements of these methods, we derive an explicit formula for the Jacobian matrix of the nonlinear Lippmann-Schwinger model which lends itself to an efficient evaluation of the gradient of the data- fidelity term. This allows us to deploy efficient methods to solve the corresponding inverse problem subject to sparsity constraints.

References in corpus (1)

• A Recursive Born Approach to Nonlinear Inverse Scattering

Cited by in corpus (7)

• Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
• Efficient and accurate inversion of multiple scattering with deep learning
• Reconstructions of refractive index tomograms via discrete algebraic reconstruction technique
• Fourier synthesis optical diffraction tomography for kilohertz rate volumetric imaging
• Method for Assessing the Fidelity of Optical Diffraction Tomography Reconstruction Methods
• Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers
• Composition-Aware Spectroscopic Tomography