High-throughput intensity diffraction tomography with a computational microscope
Not on the map. Why?
Abstract (arXiv)
We demonstrate a motion-free intensity diffraction tomography technique that enables direct inversion of 3D phase and absorption from intensity-only measurements for weakly scattering samples. We derive a novel linear forward model, featuring slice-wise phase and absorption transfer functions using angled illumination. This new framework facilitates flexible and efficient data acquisition, enabling arbitrary sampling of the illumination angles. The reconstruction algorithm performs 3D synthetic aperture using a robust, computation and memory efficient slice-wise deconvolution to achieve resolution up to the incoherent limit. We demonstrate our technique with thick biological samples having both sparse 3D structures and dense cell clusters. We further investigate the limitation of our technique when imaging strongly scattering samples. Imaging performance and the influence of multiple scattering is evaluated using a 3D sample consisting of stacked phase and absorption resolution targets. This computational microscopy system is directly built on a standard commercial microscope with a simple LED array source add-on, and promises broad applications by leveraging the ubiquitous microscopy platforms with minimal hardware modifications.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
6 papers.
- 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
- 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
- 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
- 2025Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problemscited by 6
- 2021Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samplescited by 3
- 2024The Berkeley Single Cell Computational Microscopy (BSCCM) Datasetcited by 2
- 2025Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems15 May 2025
- 2024The Berkeley Single Cell Computational Microscopy (BSCCM) Dataset9 Feb 2024
- 2022Online Deep Equilibrium Learning for Regularization by Denoising25 May 2022
- 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields27 Nov 2021
- 2021Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples29 Mar 2021
- 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees22 Jan 2021