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

Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Histogram clustering for rapid time-domain fluorescence lifetime image analysis

We propose a histogram clustering (HC) method to accelerate fluorescence lifetime imaging (FLIM) analysis in pixel-wise and global fitting modes. The proposed method's principle was demonstrated, and the combinations of HC with traditional FLIM analysis were explained. We assessed HC methods with both simulated and experimental datasets. The results reveal that HC not only increases analysis speed (up to 106 times) but also enhances lifetime estimation accuracy. Fast lifetime analysis strategies were suggested with execution times around or below 30 μs per histograms on MATLAB R2016a, 64-bit with the Intel Celeron CPU (2950M @ 2GHz).

Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD

In this paper, we develop a deep neural network based joint classification-regression approach to identify microglia, a resident central nervous system macrophage, in the brain using fluorescence lifetime imaging microscopy (FLIM) data. Microglia are responsible for several key aspects of brain development and neurodegenerative diseases. Accurate detection of microglia is key to understanding their role and function in the CNS, and has been studied extensively in recent years. In this paper, we propose a joint classification-regression scheme that can incorporate fluorescence lifetime data from two different autofluorescent metabolic co-enzymes, FAD and NADH, in the same model. This approach not only represents the lifetime data more accurately but also provides the classification engine a more diverse data source. Furthermore, the two components of model can be trained jointly which combines the strengths of the regression and classification methods. We demonstrate the efficacy of our method using datasets generated using mouse brain tissue which show that our joint learning model outperforms results on the coenzymes taken independently, providing an efficient way to classify microglia from other cells.

Rapid fluorescence lifetime estimation with modified phasor approach and Laguerre deconvolution: a comparative study

Fluorescence lifetime imaging has been shown to serve as a valuable tool for interrogating and diagnosis of biological tissue at a mesoscopic level. The ability to analyze fluorescence decay curves to extract lifetime values in real-time is crucial for clinical translation and applications such as tumor margin delineation or intracoronary imaging of atherosclerotic plaques. In this work, we compare the performance of two popular non-parametric (fit-free) methods for determining lifetime values from fluorescence decays in real-time-the Phasor approach and Laguerre deconvolution. We demonstrate results from simulated and experimental data to compare the accuracy and speed of both methods and their dependence on noise and model parameters.

Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data

Fluorescence Lifetime Imaging (FLIM) is an attractive microscopy method in the life sciences, yielding information on the sample otherwise unavailable through intensity-based techniques. A novel Noise-Corrected Principal Component Analysis (NC-PCA) method for time-domain FLIM data is presented here. The presence and distribution of distinct microenvironments are identified at lower photon counts than previously reported, without requiring prior knowledge of their number or of the dye's decay kinetics. A noise correction based on the Poisson statistics inherent to Time-Correlated Single Photon Counting is incorporated. The approach is validated using simulated data, and further applied to experimental FLIM data of HeLa cells stained with membrane dye di-4-ANEPPDHQ. Two distinct lipid phases were resolved in the cell membranes, and the modification of the order parameters of the plasma membrane during cholesterol depletion was also detected. Noise-corrected Principal Component Analysis of FLIM data resolves distinct microenvironments in cell membranes of live HeLa cells.

Towards unsupervised fluorescence lifetime imaging using low dimensional variable projection

Analyzing large fluorescence lifetime imaging (FLIM) data is becoming overwhelming; the latest FLIM systems easily produce massive amounts of data, making an efficient analysis more challenging than ever. In this paper we propose the combination of a custom-fit variable projection method, with a Laguerre expansion based deconvolution, to analyze bi-exponential data obtained from time-domain FLIM systems. Unlike nonlinear least squares methods, which require a suitable initial guess from an experienced researcher, the new method is free from manual interventions and hence can support automated analysis. Monte Carlo simulations are carried out on synthesized FLIM data to demonstrate the performance compared to other approaches. The performance is also illustrated on real-life FLIM data obtained from the study of autofluorescence of daisy pollen and the endocytosis of gold nanorods (GNRs) in living cells. In the latter, the fluorescence lifetimes of the GNRs are much shorter than the full width at half maximum of the instrument response function. Overall, our proposed method contains simple steps and shows great promise in realising automated FLIM analysis of large data sets.