Fluorescence lifetime estimation method for incomplete decay

A new incomplete decay signal model is proposed to describe the incomplete decay effects in a time- correlated single-photon counting (TCSPC) based fluorescence lifetime imaging (FLIM) system. Based on this model, we modified a MUltiple SIgnal Classification (MUSIC) algorithm to eliminate the incomplete decay effects. Monte Carlo simulations were carried out to demonstrate the performances of the proposed approach. Simulations show that the proposed method is insensitive to the laser pulse rate and has a larger lifetime dynamic range compared with previously reported approaches. As far as we know, this new method is the first non-fitting method that can resolve incomplete decay effects for multi-exponential decays

Estimating fluorescence lifetimes using the expectation-maximisation algorithm

The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method

GPU acceleration of time-domain fluorescence lifetime imaging

Fluorescence lifetime imaging microscopy (FLIM) plays a significant role in biological sciences, chemistry, and medical research. We propose a Graphic Processing Units (GPUs) based FLIM analysis tool suitable for high-speed and flexible time-domain FLIM applications. With a large number of parallel processors, GPUs can significantly speed up lifetime calculations compared to CPU-OpenMP (parallel computing with multiple CPU cores) based analysis. We demonstrate how to implement and optimize FLIM algorithms on GPUs for both iterative and non-iterative FLIM analysis algorithms. The implemented algorithms have been tested on both synthesized and experimental FLIM data. The results show that at the same precision the GPU analysis can be up to 24-fold faster than its CPU-OpenMP counterpart. This means that even for high precision but time-consuming iterative FLIM algorithms, GPUs enable fast or even real-time analysis

Optimizing Laguerre expansion based deconvolution methods for analysing bi-exponential fluorescence lifetime images

Fast deconvolution is an essential step to calibrate instrument responses in big fluorescence lifetime imaging microscopy (FLIM) image analysis. This paper examined a computationally effective least squares deconvolution method based on Laguerre expansion (LSD-LE), recently developed for clinical diagnosis applications, and proposed new criteria for selecting Laguerre basis functions (LBFs) without considering the mutual orthonormalities between LBFs. Compared with the previously reported LSD-LE, the improved LSD-LE allows to use a higher laser repetition rate, reducing the acquisition time per measurement. Moreover, we extended it, for the first time, to analyze bi-exponential fluorescence decays for more general FLIM-FRET applications. The proposed method was tested on both synthesized bi-exponential and realistic FLIM data for studying the endocytosis of gold nanorods in Hek293 cells. Compared with the previously reported constrained LSD-LE, it shows promising results

Multi-frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

This paper proposes a mixed noise model and uses the multi-frame blind deconvolution to restore the images of space objects under the Bayesian inference framework. To minimize the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise

Multi-channel, low nonlinearity time-to-digital converters based on 20nm and 28nm FPGAs

Abstract—This paper presents low nonlinearity, compact and multi-channel time-to-digital converters (TDC) in Xilinx 28nm Virtex 7 and 20nm UltraScale FPGAs. The proposed TDCs integrate several innovative methods that we have developed: 1) the sub-tapped delay line (TDL) averaging topology, 2) tap timing tests, 3) a direct compensation architecture and 4) a mixed calibration method. The code density tests show that the proposed TDCs have much better linearity performances than previously reported ones. Our approach is cost-effective in terms of the consumption of logic resources. To demonstrate this, we implemented 96 channel TDCs in both FPGAs, using less than 25 % of the logic resources. The achieved least significant bit (LSB) is 10.5ps for Virtex 7 and 5.0 ps for UltraScale FPGAs. After the compensation and calibration, the differential nonlinearity (DNL) is within [-0.05, 0.08] LSB with σDNL = 0.01 LSB, and the integral nonlinearity (INL) is within [-0.09, 0.11] LSB with σINL = 0.04 LSB for the Virtex 7 FPGA. The DNL is within [-0.12, 0.11] LSB with σDNL = 0.03 LSB, and the INL is within [-0.15, 0.48] LSB with σINL = 0.20 LSB for the UltraScale FPGA

Comment on 'A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-square deconvolution with Laguerre expansion'

This comment is to clarify that Poisson noise instead of Gaussian noise shall be included to assess the performances of least-squares deconvolution with Laguerre expansion (LSD-LE) for analysing fluorescence lifetime imaging (FLIM) data obtained from time-resolved systems. Moreover, we also corrected an equation in the paper. As the LSD-LE approach is rapid and has potential to be widely applied not only for diagnostic but for wider bioimaging applications, it is desirable to have precise noise models and equations

Estimation of fluorescence lifetimes via rotational invariance techniques

Estimation of signal parameters via rotational invariance techniques is a classical algorithm widely used in array signal processing for direction-of-arrival estimation of emitters. Inspired by this method, a new signal model and a new fluorescence lifetime estimation via rotational invariance techniques (FLERIT) were developed for multi-exponential fluorescence lifetime imaging (FLIM) experiments. The FLERIT only requires a few time bins of a histogram generated by a time-correlated single photon counting FLIM system, greatly reducing the data throughput from the imager to the signal processing units. As a non-iterative method, the FLERIT does not require initial conditions, prior information nor model selection that are usually required by widely used traditional fitting methods, including nonlinear least square methods or maximum likelihood methods. Moreover, its simplicity means it is suitable for implementations in embedded systems for real-time applications. FLERIT was tested on synthesized and experimental fluorescent cell data showing the potentials to be widely applied in FLIM data analysis

Theoretical investigations of a modified compressed ultrafast photography method suitable for single-shot fluorescence lifetime imaging

A single-shot fluorescence lifetime imaging (FLIM) method based on the compressed ultrafast photog- raphy (CUP) is proposed, named space-restricted CUP (srCUP). srCUP is suitable for imaging objects moving slowly (< ∼ 150/M mm/s, M is the magnification of the objective lens) in the field of view with the intensity changing within nanoseconds in a measurement window around 10 ns. We used synthetic datasets to explore the performances of srCUP compared with CUP and TCUP (a variant of CUP). srCUP not only provides superior reconstruction performances, but its reconstruction time is also two- and three- fold faster than CUP and TCUP, respectively. The lifetime determination performances were assessed by estimating lifetime components, amplitude- and intensity-weighted average lifetimes (τA and τI) with the reconstructed scenes using the least square method based on a bi-exponential model. srCUP has the best accuracy and precision for lifetime determinations with a relative bias less than 7% and a coefficient of variation less than 7% for τA and a relative bias less than 10% and a coefficient of variation less than 11% for τI

A rapid analysis platform for investigating the cellular locations of bacteria using two-photon fluorescence lifetime imaging microscopy

Facultative intracellular pathogens are able to live inside and outside host cells. It is highly desirable to differentiate their cellular locations for the purposes of fundamental research and clinical applications. In this work, we developed a novel analysis platform that allows users to choose two analysis models: amplitude weighted lifetime (τA) and intensity weighted lifetime (τI) for fluorescence lifetime imaging microscopy (FLIM). We applied these two models to analyse FLIM images of mouse Raw macrophage cells that were infected with bacteria Shigella Sonnei, adherent and invasive E. coli (AIEC) and Lactobacillus. The results show that the fluorescence lifetimes of bacteria depend on their cellular locations. The τA model is superior in visually differentiating bacteria that are in extra- and intra-cellular and membrane-bounded locations, whereas the τI model show excellent precision. Both models show speedy performances that analysis can be performed within 0.3 second. We also compared the proposed models with a widely used commercial software tool (τC, SPC Image, Becker & Hickl GmbH), showing similar τI and τC results. The platform also allows users to perform phasor analysis with great flexibility to pinpoint the regions of interest from lifetime images as well as phasor plots. This platform holds the disruptive potential of replacing z-stack imaging for identifying intracellular bacteria