Accelerated High-Resolution 3D Refractive Index Reconstruction Using Holographic Incoherent-Light-Source QPI and Deep Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F26%3A0199607" target="_blank" >RIV/00216305:26620/26:0199607 - isvavai.cz</a>

Result on the web

<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13329/133290H/Accelerated-high-resolution-3D-refractive-index-reconstruction-using-holographic-incoherent/10.1117/12.3041129.full" target="_blank" >https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13329/133290H/Accelerated-high-resolution-3D-refractive-index-reconstruction-using-holographic-incoherent/10.1117/12.3041129.full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.3041129" target="_blank" >10.1117/12.3041129</a>

Result language

angličtina

Original language name

Accelerated High-Resolution 3D Refractive Index Reconstruction Using Holographic Incoherent-Light-Source QPI and Deep Learning

Original language description

Quantitative Phase Imaging (QPI) offers 2D label-free live cell observations. To satisfy the burgeoning need for expanding 2D QPI for the ability of 3D refractive index distribution (RID) reconstruction, an approach known as Holographic Tomography (HT) has been developed. Our work proposes an alternative 3D RID reconstruction method, utilizing a z-stack of phase images obtained by the Holographic Incoherent-light-source QPI (hiQPI). Precise reconstruction of a 3D RID from z-stacked hiQPI phase images represents an inverse problem. This inverse problem can be solved either by physics-driven iterative algorithms or by a dataset-driven approach, i.e. by leveraging trained neural networks. Though the physics-driven algorithms are more established, the dataset-driven algorithms can significantly reduce the reconstruction time. We present a rapid dataset-driven 3D reconstruction algorithm utilizing a U-net-based convolutional neural network (CNN). The CNN is trained on a dataset comprising various simulated red blood cells (RBC) and corresponding simulated hiQPI z-stacks. RBCs are generated with varying parameters and refractive indices. Furthermore, to enlarge the dataset, the RBCs are augmented with affine transformations, including rotation, elastic deformation, Gaussian noise insertion, and blur. The hiQPI z-stacks are simulated employing the multi-slice beam propagation method in conjunction with the underlying hiQPI theory. This study demonstrates a novel alternative approach to the 3D RID reconstruction method, utilizing a z-stack of hiQPI phase images and a fast, high-quality reconstruction algorithm based on supervised deep learning. However, the results should be thoroughly validated against physics-based approaches in the future.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10300 - Physical sciences

Project

<a href="/en/project/GA24-12283S" target="_blank" >GA24-12283S: Boosting incoherent quantitative phase imaging by implementing 3D-image reconstruction</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Progress in Biomedical Optics and Imaging Proceedings of SPIE

ISBN

9781510684065

ISSN

0277-786X

e-ISSN

1996-756X

Number of pages

10

Pages from-to

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Publisher name

SPIE

Place of publication

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Event location

San Francisco

Event date

Jan 25, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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