Evidence map›Paper›PMID 38293292›Full record

ArticleJournal of biomedical optics2024

TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.

Vikas Thapa, Ashwini Subhash Galande, Gurram Hanu Phani Ram, Renu John

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Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Vikas ThapaIndian Institute of Technology Hyderabad, Medical Optics and Sensors Laboratory, Department of Biomedical Engineering, Hyderabad, Telangana, India.
Ashwini Subhash GalandeIndian Institute of Technology Hyderabad, Medical Optics and Sensors Laboratory, Department of Biomedical Engineering, Hyderabad, Telangana, India.ORCID 0000-0001-7318-0004
Gurram Hanu Phani RamIndian Institute of Technology Hyderabad, Medical Optics and Sensors Laboratory, Department of Biomedical Engineering, Hyderabad, Telangana, India.
Renu JohnIndian Institute of Technology Hyderabad, Medical Optics and Sensors Laboratory, Department of Biomedical Engineering, Hyderabad, Telangana, India.ORCID 0000-0003-3254-2472

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: Artificial intelligence (AI) has become a prominent technology in computational imaging over the past decade. The expeditious and label-free characteristics of quantitative phase imaging (QPI) render it a promising contender for AI investigation. Though interferometric methodologies exhibit potential efficacy, their implementation involves complex experimental platforms and computationally intensive reconstruction procedures. Hence, non-interferometric methods, such as transport of intensity equation (TIE), are preferred over interferometric methods. Aim: TIE method, despite its effectiveness, is tedious as it requires the acquisition of many images at varying defocus planes. The proposed methodology holds the ability to generate a phase image utilizing a single intensity image using generative adversarial networks (GANs). We present a method called TIE-GANs to overcome the multi-shot scheme of conventional TIE. Approach: The present investigation employs the TIE as a QPI methodology, which necessitates reduced experimental and computational efforts. TIE is being used for the dataset preparation as well. The proposed method captures images from different defocus planes for training. Our approach uses an image-to-image translation technique to produce phase maps and is based on GANs. The main contribution of this work is the introduction of GANs with TIE (TIE:GANs) that can give better phase reconstruction results with shorter computation times. This is the first time the GANs is proposed for TIE phase retrieval. Results: The characterization of the system was carried out with microbeads of Conclusions: The proposed methodology holds the ability to generate a phase image utilizing a single intensity image. Our method is feasible for digital cytology because of its reported high value of SSIM. Our approach can handle defocused images in such a way that it can take intensity image from any defocus plane within the provided range and able to generate phase map.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedDiagnostic ImagingMicrospheresQuantitative Phase Imagingdeep learninggenerative adversarial networksphase mapquantitative phase imagingtransport of intensity equation

Identifiers

PMID38293292
PMCPMC10826717

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.