Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.

Qualitative and quantitative measurements of complex flows demand for fast single-shot fluorescence lifetime imaging (FLI) technology with high precision. A method, single-shot time-gated fluorescence lifetime imaging using three-frame images (TFI-TGFLI), is presented. To our knowledge, it is the first work to combine a three-gate rapid lifetime determination (RLD) scheme and a four-channel framing camera to achieve this goal. Different from previously proposed two-gate RLD schemes, TFI-TGFLI can provide a wider lifetime range 0.6 ~ 13ns with reasonable precision. The performances of the proposed approach have been examined by both Monte-Carlo simulations and toluene seeded gas mixing jet diagnosis experiments. The measured average lifetimes of the whole excited areas agree well with the results obtained by the streak camera, and they are 7.6ns (N₂ = 7L/min; O₂ < 0.1L/min) and 2.6ns (N₂ = 19L/min; O₂ = 1L/min) with the standard deviations of 1.7ns and 0.8ns among the lifetime image pixels, respectively. The concentration distributions of the quenchers and fluorescent species were further analyzed, and they are consistent with the experimental settings.

Fluorescence lifetime imaging (FLIM) is powerful for monitoring cellular microenvironments, protein conformational changes, and protein interactions. It can facilitate metabolism research, drug screening, DNA sequencing, and cancer diagnosis. Lifetime determination algorithms (LDAs) adopted in FLIM analysis can influence biological interpretations and clinical diagnoses. Herein, we discuss the commonly used and advanced time-domain LDAs classified in fitting and non-fitting categories. The concept and explicit mathematical forms of LDAs are reviewed. The output lifetime parameter types are discussed, including lifetime components, average lifetimes, and graphic representation. We compare their performances, identify trends, and provide suggestions for end users in terms of multi-exponential decay unmixing ability, lifetime estimation precision, and processing speed.

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).

A single-shot fluorescence lifetime imaging (FLIM) method based on the compressed ultrafast photography (CUP) is proposed, named space-restricted CUP (srCUP). srCUP is suitable for imaging objects moving slowly ( <∼ 150 / M m m / 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 speed is also twofold and threefold faster than CUP and TCUP, respectively. The lifetime determination performances were assessed by estimating lifetime components, amplitude- and intensity-weighted average lifetimes ( τ A and τ I ), with the reconstructed scenes using the least squares 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 τ A , and a relative bias less than 10% and a coefficient of variation less than 11% for τ I .

The frequency recognition algorithm for multiple exposures (FRAME) is a spatial frequency multiplexing method that enables high-speed videography with high spatial resolution across a wide field of view and high temporal resolution up to femtoseconds. The criterion to design encoded illumination pulses is an essential factor that affects the sequence depth and reconstruction accuracy of FRAME but was not previously discussed. When the spatial frequency is exceeded, the fringes on digital imaging sensors can become distorted. To exploit the Fourier domain for FRAME with deep sequences and avoid fringe distortion, the maximum Fourier map for sequence arrangement was determined to be a diamond shape. The maximum axial frequency should be a quarter of the sampling frequency of digital imaging sensors. Based on this criterion, the performances of reconstructed frames were theoretically investigated by considering arrangement and filtering methods. To ensure optimal and uniform interframe quality, the frames near the zero frequency should be removed and optimized super-Gaussian filters should be employed. Experiments were conducted flexibly with a digital mirror device to generate illumination fringes. Following these suggestions, the movement of a water drip dropping on a water surface was captured with 20 and 38 frames with uniform interframe quality. The results prove the effectiveness of the proposed methods to improve the reconstruction accuracy and promote the development of FRAME with deep sequences.(c) 2023 Optica Publishing Group