Solving the missing cone problem by deep learning

Dashan Dong,Kebin Shi

doi:10.1117/1.ap.2.2.020501

Abstract

A commentary on the article “Three-dimensional tomography of red blood cells using deep learning” by J. Lim, A. Ayoub, and D. Psaltis, Adv. Photonics Volume 2, Issue 2, doi: 10.1117/1.AP.2.2.026001.

Highlights

In order to extract the quantitative three-dimensional (3-D) distribution of refractive index (RI) in live cells noninvasively, optical diffraction tomography (ODT) uses the non-ionizing light sources instead of x-rays to perform a computational holographic tomography
ODT reconstructs the 3-D distribution of RI by casting scattering spectrum caps from different rotation directions together, which leads to a range of missing data in a spatial frequency domain along the rotation axis of illumination or a sample: the so-called “missing cone.”
Solving the missing cone problem is a crucial step for widening ODT applications in cell biology research and 3-D imaging of other subwavelength structures

Summary

Introduction

In order to extract the quantitative three-dimensional (3-D) distribution of refractive index (RI) in live cells noninvasively, optical diffraction tomography (ODT) uses the non-ionizing light sources instead of x-rays to perform a computational holographic tomography. Downloaded From: https://www.spiedigitallibrary.org/journals/Advanced-Photonics on 08 Nov 2021 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use tomographic rotation of incident beam [Fig. 1(a)] or sample [Fig. 1(b)] is always confined by a limited NA of the objective or a working distance of the imaging optics.

Results

Conclusion