Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy

Wang, Shulei; Chacko, Jenu V.; Sagar, Abdul K.; Eliceiri, Kevin W.; Yuan, Ming

doi:10.1364/boe.10.005497

Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy

Journal Article · Tue Jan 01 00:00:00 EST 2019 · Biomedical Optics Express

DOI:https://doi.org/10.1364/boe.10.005497· OSTI ID:1569103

^[1]; Chacko, Jenu V. ^[2]; Sagar, Abdul K. ^[2]; ^[3]; Yuan, Ming ^[4]

Univ. of Pennsylvania, Philadelphia, PA (United States). Perelman School of Medicine. Dept. of Biostatistics, Epidemiology and Informatics
Univ. of Wisconsin, Madison, WI (United States). Lab. for Optical and Computational Instrumentation
Univ. of Wisconsin, Madison, WI (United States). Lab. for Optical and Computational Instrumentation; Morgridge Inst. for Research, Madison, WI (United States)
Columbia Univ., New York, NY (United States). Dept. of Statistics

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.

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Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE Office of Science (SC); USDOE

Grant/Contract Number:: SC0019013

OSTI ID:: 1569103

Alternate ID(s):: OSTI ID: 1803493

Journal Information:: Biomedical Optics Express, Vol. 10, Issue 11; ISSN 2156-7085

Publisher:: Optical Society of America (OSA)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 15 works

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