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

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

Improving Fluorescence Lifetime Imaging Microscopy Deconvolution Using Constrained Laguerre Basis Functions

Fluorescence lifetime imaging microscopy (FLIM) is a noninvasive invasive optical imaging modality which is finding new applications in medical imaging. In FLIM, the fluorescence time decay is measured at a pixel. The fluorescence impulse response function (IRF) is then estimated using a deconvolution of the instrument response and the measured fluorescence time decay. Two of the challenges facing FLIM are speed of the deconvolution and the accuracy of the IRFs. The linear expansion of the fluorescence decays based on the orthonormal Laguerre basis functions (LBFs) is among the fastest methods for estimating the IRFs. The automated implementation to optimize the Laguerre parameter improves the speed of the deconvolution using the LBFs but uses a global optimization. Therefore, the IRFs do not necessarily mimic exponential time decays, or monotonically decreasing functions. On the other hand, applying a constraint to the LBFs using the Active Set Nonnegative Least Squares (NNLS) method improves the IRF estimation. The estimation of the Laguerre parameter using the NNLS method, however, is about 10-15x slower. By combining these two deconvolution techniques, we found that the deconvolution time is similar to the automated global Laguerre parameter deconvolution while the IRF estimation always results in a monotonically decreasing function

Descomposición de datos multi-espectrales: interfaz gráfica para Matlab

Avances recientes han permitido el desarrollo de dispositivos capaces de capturar información en múltiples longitudes de onda. Estos datos tienen diversas aplicaciones con el problema en común de cómo interpretarlos. Una de las técnicas utilizadas con este fin es la descomposición espectral, que separa los datos de una muestra en sus componentes básicos y concentraciones proporcionales. Nuestro trabajo previo ha estado enfocado en la descomposición espectral de datos de fluorescencia multiespectral, donde se han desarrollado métodos que proporcionan una solución cuantitativa, robusta y rápida, la cual no está limitada por el número de componentes que se pueden caracterizar. En este trabajo, presentamos una interface desarrollada en Matlab que puede estimar los perfiles característicos de los componentes constituyentes de una muestra y sus abundancias. En caso de que no se tenga información alguna sobre la muestra, nos permite obtener además el número de componentes en ella. El artículo hace una descripción del software y sus herramientas.Además, se ejemplifica su uso en la caracterización de muestras ex-vivo de arterias coronarias. El programa se encuentra disponible de manera gratuita y provee al usuario de una herramienta fácil de usar para el análisis de datos multi o hiper-espectrales.Palabra(s) Clave(s): descomposición ciega, fluorescencia endógena, interfaz gráfica, optimización cuadrática, quimiometría

FLIM data analysis based on Laguerre polynomial decomposition and machine-learning

Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components

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

On synthetic instrument response functions of time-correlated single-photon counting based fluorescence lifetime imaging analysis

Time-correlated single-photon counting (TCSPC) has been the gold standard for fluorescence lifetime imaging (FLIM) techniques due to its high signal-to-noize ratio and high temporal resolution. The sensor system's temporal instrument response function (IRF) should be considered in the deconvolution procedure to extract the real fluorescence decay to compensate for the distortion on measured decays contributed by the system imperfections. However, to measure the instrument response function is not trivial, and the measurement setup is different from measuring the real fluorescence. On the other hand, automatic synthetic IRFs can be directly derived from the recorded decay profiles and provide appropriate accuracy. This paper proposed and examined a synthetic IRF strategy. Compared with traditional automatic synthetic IRFs, the new proposed automatic synthetic IRF shows a broader dynamic range and better accuracy. To evaluate its performance, we examined simulated data using nonlinear least square deconvolution based on both the Levenberg-Marquardt algorithm and the Laguerre expansion method for bi-exponential fluorescence decays. Furthermore, experimental FLIM data of cells were also analyzed using the proposed synthetic IRF. The results from both the simulated data and experimental FLIM data show that the proposed synthetic IRF has a better performance compared to traditional synthetic IRFs. Our work provides a faster and precise method to obtain IRF, which may find various FLIM-based applications. We also reported in which conditions a measured or a synthesized IRF can be applied

Supervised Machine Learning Algorithms for Early Detection of Oral Epithelial Cancer Using Fluorescence Lifetime Imaging Microscopy

In this study, the clinical potential of the endogenous multispectral Fluorescence lifetime imaging microscopy (FLIM) was investigated to objectively detect oral cancer. To this end, in vivo FLIM imaging was performed on a hamster cheek pouch model with an oral epithelial cancer. The autofluorescence emissions of the hamster tissue were recorded in three different spectral bands which were determined based on the peak emission wavelength of three major fluorophores of hamster mucosal tissue: collagen (390±20 nm), NADH (452±22.5 nm), and FAD (>500 nm). Then, a total of 7 features pertaining to FLIM were extracted from each channel, providing 21 features overall. To design a classifier in a supervised approach, a training set is required, in which each pixel is labeled with one of the four groups. In this study, we utilized a total of 65 regions of interest (ROI) from the imaged cheek pouch of seven hamsters, for which the histopathological diagnosis could be correlated. The resulting database was used to train a K-Nearest-Neighborhood (KNN) algorithm aimed to detect benign from pre-malignant/malignant lesions. In addition, a Sequential Floating Forward Selection (SFFS) was applied to optimize the KNN algorithm and identify a subset of features that would maximize the classification performance. The best performance corresponded to the 3-NN algorithm with the (1/e) lifetime in the NADH channel and the normalized intensity in FAD channel as features. The overall accuracy, sensitivity and specificity for detecting pre-malignant and malignant lesions were 92.2%, 87.3%, and 94%, respectively, assessed using a cross-validation method. It has to be noted that the feature selection algorithm suggested both lifetime parameter and intensity parameter for an optimal feature set, which validates the need to utilize endogenous FLIM for the objective detection of oral cancer. At last, all data from the 65 ROIs were used to train the 3NN classifier to classify the full tissue areas. The results suggest that multispectral endogenous FLIM has a potential to screen malignant oral epithelial tissue. This technology, however, still needs to be evaluated in human patients

Fluorescence Lifetime Imaging Microscopy (FLIM) System for Imaging of Oral Cancer and Precancer

Standard diagnosis of oral cancer is based on visual inspection and palpation by a clinician followed by histological examination of one or more tissue biopsies. Choosing the right location for biopsies, which represents the most severe lesion, is difficult and subjective to each clinician’s experience, especially for precancer lesions which are often diffuse, multifocal, and clinically indistinguishable from benign lesions. This may lead to low diagnosis sensitivity. The aim of this dissertation is to design a more sensitive and objective screening tool to guide the biopsy of oral cancer and precancer. Fluorescence lifetime imaging microscopy (FLIM) is a noninvasive optical technique which is able to detect the information of tissue metabolism and biochemistry based on fluorescence as a source of contrast. Recently, there is increasing interest in the application of multispectral FLIM for medical diagnosis. Central to the clinical translation of FLIM technology is the development of compact and high-speed clinically compatible systems. In this dissertation, four multispectral FLIM systems were designed and built. A bench-top multispectral FLIM system was first built and combined with reflectance confocal microscopy (RCM) for the preclinical validation by imaging a hamster cheek pouch model of oral carcinogenesis. After that, in order to facilitate in vivo imaging of human oral mucosa, three different multispectral FLIM endoscopes were designed. The first FLIM endoscope was built based on a fiber bundle and the time-gated implementation by an intensified charged-coupled device (ICCD). The system was validated by imaging a hamster cheek pouch model of oral carcinogenesis. To achieve faster imaging speed and more accurate lifetime estimation, two rigid handheld FLIM endoscopes were built based on a pulse sampling implementation. These two handheld endoscopes were different in weight and size. The more compact one might serve as the clinical prototype for oral cancer and precancer detection. Both systems were validated by imaging the human oral biopsy ex vivo and human oral mucosa in vivo. The development of these systems will facilitate the evaluation of multispectral FLIM for oral cancer and precancer detection

Novel Technologies for Real-Time Fluorescent Lifetime Imagind Data Acquisition and Processing for Clinical Diagnosis

Endogenous Fluorescence Lifetime Imaging (FLIM) is a noninvasive technique that has been explored with promising results in a wide range of biomedical applications, including clinical diagnosis. A central issue for the translation of FLIM into the medical field is the development of a robust, fast and cost-effective FLIM instrumentation suitable for in vivo tissue imaging. This thesis directly addressed some of the technical limitations that must be overcome to enable clinical applications of FLIM. The following specific aims were accomplished. First, endogenous FLIM imaging and high-resolution reflectance confocal microscopy (RCM) were integrated into a multimodal bench-top optical system. This multimodal system was used to image oral epithelial cancer in a hamster cheek pouch model. Second, an endoscopic system for fast (0.5-4 frames/second) endogenous wide-field FLIM imaging of oral lesions was developed. The FLIM endoscope system is being evaluate at Texas A&M University College of Dentistry, where more than 80 patients presenting oral lesions suspected of pre-cancer or cancer have been imaged up to date. Third, a novel fluorescence lifetime estimation algorithm was developed to achieve robust, accurate, and real-time fluorescence lifetime estimation. This algorithm is enabling real-time FLIM image processing and visualization during the endoscopic examination of patients with suspicious oral lesions. Finally, the endoscopic endogenous FLIM data from suspicious oral lesions collected at the Texas A&M College of Dentistry was used to develop machine learning algorithms for automated identification of precancerous and cancerous lesions from benign oral epithelial lesions. Our results indicate that endogenous FLIM endoscopy can detect oral epithelial pre-cancer and cancer from a wider range of benign conditions, with levels of sensitivity and specificity above 85%. Altogether, this work has demonstrated the potentials of endogenous FLIM endoscopy as a clinical tool for early detection of oral epithelial cancer