A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile.

Time-resolved fluorescence imaging techniques, like confocal fluorescence lifetime imaging microscopy, are powerful photonic instrumentation tools of modern science with diverse applications, including: biology, medicine, and chemistry. However, complexities of the systems, both at specimen and device levels, cause difficulties in quantifying soft biomarkers. To address the problems, we first aim to understand and model the underlying photophysics of fluorescence decay curves. For this purpose, we provide a set of mathematical functions, called "life models", fittable with the real temporal recordings of histogram of photon counts. For each model, an equivalent electrical circuit, called a "life circuit", is derived for explaining the whole process. In confocal endomicroscopy, the components of excitation laser, specimen, and fluorescence-emission signal as the histogram of photon counts are modelled by a power source, network of resistor-inductor-capacitor circuitry, and multimetre, respectively. We then design a novel pixel-level temporal classification algorithm, called a "fit-flexible approach", where qualities of "intensity", "fall-time", and "life profile" are identified for each point. A model selection mechanism is used at each pixel to flexibly choose the best representative life model based on a proposed Misfit-percent metric. A two-dimensional arrangement of the quantified information detects some kind of structural information. This approach showed a potential of separating microbeads from lung tissue, distinguishing the tri-sensing from conventional methods. We alleviated by 7% the error of the Misfit-percent for recovering the histograms on real samples than the best state-of-the-art competitor. Codes are available online.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review.

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Fast and Robust Single-Exponential Decay Recovery From Noisy Fluorescence Lifetime Imaging.

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an "amplitude-lifetime" game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers.