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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10300 - Physical sciences

Projekt

<a href="/cs/project/GA24-12283S" target="_blank" >GA24-12283S: Posílení nekoherentního kvantitativního fázového zobrazování zavedením rekonstrukce trojrozměrného obrazu</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Progress in Biomedical Optics and Imaging Proceedings of SPIE

ISBN

9781510684065

ISSN

0277-786X

e-ISSN

1996-756X

Počet stran výsledku

10

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

—

Místo konání akce

San Francisco

Datum konání akce

25. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—