Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy

Abstract

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 29, 2021
Source ID
10.1364/oe.439675

Entities

People

  • Eric J. Chaney
  • Haohua Tu
  • Janet E. Sorrells
  • Lingxiao Yang
  • Marina Marjanovic
  • Rishyashring R Iyer
  • Stephen A. Boppart

Organizations

  • Air Force Office of Scientific Research
  • National Institutes of Health
  • National Science Foundation
  • University of Illinois Urbana–Champaign

Tags

Fields of Study

  • Physics

Readers

  • Cellular and Molecular Pathways of Apoptosis.
  • Medical Imaging.
  • Regression Analysis.