Presentation + Paper
12 March 2024 Neural network assisted fast refractive index tomography in thick tissues with quantitative oblique back-illumination microscopy
Author Affiliations +
Abstract
Quantitative phase imaging (QPI) has emerged as a valuable method in biomedical research by providing label-free, high-resolution phase distribution of transparent cells and tissues. While QPI is limited to transparent samples, quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. This technology employs four oblique back illumination images taken at the same focal planes, along with a rapid 2D deconvolution reconstruction algorithm, to generate 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be utilized to produce 3D RI tomograms, offering enhanced RI quantitative accuracy. However, 3D RI generation requires a more computationally intensive reconstruction process, preventing its potential of a real-time 3D RI tomography. In this paper, we propose a neural network-involved reconstruction technique that significantly reduces the processing time to a third while maintaining high fidelity compared to the deconvolution-based results.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zhenmin Li, Paloma Casteleiro Costa, Zhe Guang, and Francisco E. Robles "Neural network assisted fast refractive index tomography in thick tissues with quantitative oblique back-illumination microscopy", Proc. SPIE 12854, Label-free Biomedical Imaging and Sensing (LBIS) 2024, 128540J (12 March 2024); https://doi.org/10.1117/12.3003414
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
3D image processing

Image deconvolution

Tomography

Neural networks

3D image reconstruction

Biological samples

Deconvolution

Back to Top