Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool used to study the molecular environment of flurophores. In time domain FLIM, extracting lifetime from fluorophores signals entails fitting data to a decaying exponential distribution function. However, most existing techniques for this purpose need large amounts of photons at each pixel and a long computation time, thus making it difficult to obtain reliable inference in applications requiring either short acquisition or minimal computation time. In this work, we introduce a new nonparametric empirical Bayesian framework for FLIM data analysis (NEB-FLIM), leading to both improved pixel-wise lifetime estimation and a more robust and computationally efficient integral property inference. This framework is developed based on a newly proposed hierarchical statistical model for FLIM data and adopts a novel nonparametric maximum likelihood estimator to estimate the prior distribution. To demonstrate the merit of the proposed framework, we applied it on both simulated and real biological datasets and compared it with previous classical methods on these datasets.
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1569103
- Alternate Identifier(s):
- OSTI ID: 1803493
- Grant/Contract Number:
- SC0019013
- Resource Type:
- Published Article
- Journal Name:
- Biomedical Optics Express
- Additional Journal Information:
- Journal Name: Biomedical Optics Express Journal Volume: 10 Journal Issue: 11; Journal ID: ISSN 2156-7085
- Publisher:
- Optical Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; Biochemistry & Molecular Biology; Optics; Radiology, Nuclear Medicine & Medical Imaging
Citation Formats
Wang, Shulei, Chacko, Jenu V., Sagar, Abdul K., Eliceiri, Kevin W., and Yuan, Ming. Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy. United States: N. p., 2019.
Web. doi:10.1364/BOE.10.005497.
Wang, Shulei, Chacko, Jenu V., Sagar, Abdul K., Eliceiri, Kevin W., & Yuan, Ming. Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy. United States. https://doi.org/10.1364/BOE.10.005497
Wang, Shulei, Chacko, Jenu V., Sagar, Abdul K., Eliceiri, Kevin W., and Yuan, Ming. Thu .
"Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy". United States. https://doi.org/10.1364/BOE.10.005497.
@article{osti_1569103,
title = {Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy},
author = {Wang, Shulei and Chacko, Jenu V. and Sagar, Abdul K. and Eliceiri, Kevin W. and Yuan, Ming},
abstractNote = {Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool used to study the molecular environment of flurophores. In time domain FLIM, extracting lifetime from fluorophores signals entails fitting data to a decaying exponential distribution function. However, most existing techniques for this purpose need large amounts of photons at each pixel and a long computation time, thus making it difficult to obtain reliable inference in applications requiring either short acquisition or minimal computation time. In this work, we introduce a new nonparametric empirical Bayesian framework for FLIM data analysis (NEB-FLIM), leading to both improved pixel-wise lifetime estimation and a more robust and computationally efficient integral property inference. This framework is developed based on a newly proposed hierarchical statistical model for FLIM data and adopts a novel nonparametric maximum likelihood estimator to estimate the prior distribution. To demonstrate the merit of the proposed framework, we applied it on both simulated and real biological datasets and compared it with previous classical methods on these datasets.},
doi = {10.1364/BOE.10.005497},
journal = {Biomedical Optics Express},
number = 11,
volume = 10,
place = {United States},
year = {2019},
month = {10}
}
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https://doi.org/10.1364/BOE.10.005497
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