Bulletin of the American Physical Society
APS April Meeting 2019
Volume 64, Number 3
Saturday–Tuesday, April 13–16, 2019; Denver, Colorado
Session J11: Physics in medicine: devices, algorithms, and models
1:30 PM–3:18 PM,
Sunday, April 14, 2019
Sheraton
Room: Governor's Square 17
Sponsoring
Unit:
GMED
Chair: Robert Jeraj, University of Wisconsin, Madison
Abstract: J11.00004 : Advancing Complex Fluorescence Lifetime Image Reconstruction via Deep 3D-FCNs*
2:06 PM–2:18 PM
Presenter:
Jason T. Smith
(Rensselaer Polytechnic Institute)
Authors:
Jason T. Smith
(Rensselaer Polytechnic Institute)
Ruoyang Yao
(Rensselaer Polytechnic Institute)
Nattawut Sinsuebphon
(National Electronics and Computer Technology Center (NECTEC))
Alena Rudkouskaya
(Albany Medical College)
Margarida Barroso
(Albany Medical College)
Pingkun Yan
(Rensselaer Polytechnic Institute)
Xavier Intes
(Rensselaer Polytechnic Institute)
Fluorescence lifetime imaging (FLI) is a mainstay imaging technique with the unique benefit of enabling sensitive quantification of the biological micro-environment, such as with metabolic status and reactive oxygen species. Moreover, FLI has an increasing role in preclinical and clinical settings thanks to its ability to reveal tissue composition, to enable biomarker multiplex-imaging and to provide enhanced data sets for tomographic applications. Further, FLI is the most robust approach to perform Förster Resonance Energy Transfer (FRET) which recently has been demonstrated to enable the quantification of target-receptor engagement in live subject. However, the technique is somewhat limited due in large part to its reliance on time-consuming inverse solvers. Herein, we demonstrate a novel fit-free approach for FLI reconstruction based on a 3D-Fully Convolutional Network (3D-FCN) trained entirely in silico capable of high-performance quantification of complex fluorescence decays simultaneously over a whole image in quasi real-time. Our microscopic (FLIM) and macroscopic (MFLI) studies establish our analytic framework as a robust tool for FLI studies over a large range of lifetimes (visible-NIR), photon count and technologies employed.
*NIH Grants R01 EB19443 and R01 CA207725
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