Omar Gutierrez-Navarro

Blind End-Member and Abundance Extraction for Multispectral Fluorescence Lifetime Imaging Microscopy Data

This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.

Sleep-wake detection based on respiratory signal acquired through a Pressure Bed Sensor

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique.

A Fully Constrained Optimization Method for Time-Resolved Multispectral Fluorescence Lifetime Imaging Microscopy Data Unmixing

This paper presents a new unmixing methodology of multispectral fluorescence lifetime imaging microscopy (m-FLIM) data, in which the spectrum is defined as the combination of time-domain fluorescence decays at multiple emission wavelengths. The method is based on a quadratic constrained optimization (CO) algorithm that provides a closed-form solution under equality and inequality restrictions. In this paper, it is assumed that the time-resolved fluorescence spectrum profiles of the constituent components are linearly independent and known a priori. For comparison purposes, the standard least squares (LS) solution and two constrained versions nonnegativity constrained least squares (NCLS) and fully constrained least squares (FCLS) (Heinz and Chang, 2001) are also tested. Their performance was evaluated by using synthetic simulations, as well as imaged samples from fluorescent dyes and ex vivo tissue. In all the synthetic evaluations, the CO obtained the best accuracy in the estimations of the proportional contributions. CO could achieve an improvement ranging between 41% and 59% in the relative error compared to LS, NCLS, and FCLS at different signal-to-noise ratios. A liquid mixture of fluorescent dyes was also prepared and imaged in order to provide a controlled scenario with real data, where CO and FCLS obtained the best performance. The CO and FCLS were also tested with 20 ex vivo samples of human coronary arteries, where the expected concentrations are qualitatively known. A certainty measure was employed to assess the confidence in the estimations made by each algorithm. The experiments confirmed a better performance of CO, since this method is optimal with respect to equality and inequality restrictions in the linear unmixing formulation. Thus, the evaluation showed that CO achieves an accurate characterization of the samples. Furthermore, CO is a computational efficient alternative to estimate the abundance of components in m-FLIM data, since a global optimal solution is always guaranteed in a closed form.

Estimation of the number of fluorescent end-members for quantitative analysis of multispectral FLIM data

Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.

A new method to estimate abundances of multiple components using multi-spectral Fluorescence Lifetime Imaging Microscopy

Multi-Spectral Fluorescent Lifetime Imaging Microscopy (m-FLIM) is a technique that aims to perform noninvasive in situ clinical diagnosis of several diseases. It measures the endogenous fluorescence of molecules, recording their lifetime decay in different wavelength bands. This signal is a mixed response of multiple fluorescent components present in a tissue sample. The goal is to decompose the mixture and estimate the proportional contributions of its constituents. Estimation of such quantitative description will help to characterize the molecular constitution of a given sample. This paper presents a new method to estimate the abundances of multiple components present in a mixture measured using m-FLIM data. It provides a closed-form solution under the fully constrained linear unmixing model and assuming the number of components as well as their ideal lifetime decays are known. Its performance is tested using synthetic samples with three components, where performance can be measured accurately and the percentage error is around 6%. The algorithm was also validated performing unmixing of ex vivo data samples from atherosclerotic human tissue containing collagen, elastin and low-density lipoproteins. These experiments were validated against ground-truth maps, which only give a quantitative description, and the estimated accuracy was around 88%.

Quadratic blind linear unmixing

Spectral unmixing is the process of breaking down data from a sample into its basic components and their abundances. Previous work has been focused on blind unmixing of multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) datasets under a linear mixture model and quadratic approximations. This method provides a fast linear decomposition and can work without a limitation in the maximum number of components or end-members. Hence this work presents an interactive software which implements our blind end-member and abundance extraction (BEAE) and quadratic blind linear unmixing (QBLU) algorithms in Matlab. The options and capabilities of our proposed software are described in detail. When the number of components is known, our software can estimate the constitutive end-members and their abundances. When no prior knowledge is available, the software can provide a completely blind solution to estimate the number of components, the end-members and their abundances. The characterization of three case studies validates the performance of the new software: ex-vivo human coronary arteries, human breast cancer cell samples, and in-vivo hamster oral mucosa. The software is freely available in a hosted webpage by one of the developing institutions, and allows the user a quick, easy-to-use and efficient tool for multi/hyper-spectral data decomposition.

Blind deconvolution estimation of fluorescence measurements through quadratic programming

Abstract. Time-deconvolution of the instrument response from fluorescence lifetime imaging microscopy (FLIM) data is usually necessary for accurate fluorescence lifetime estimation. In many applications, however, the instrument response is not available. In such cases, a blind deconvolution approach is required. An iterative methodology is proposed to address the blind deconvolution problem departing from a dataset of FLIM measurements. A linear combination of a base conformed by Laguerre functions models the fluorescence impulse response of the sample at each spatial point in our formulation. Our blind deconvolution estimation (BDE) algorithm is formulated as a quadratic approximation problem, where the decision variables are the samples of the instrument response and the scaling coefficients of the basis functions. In the approximation cost function, there is a bilinear dependence on the decision variables. Hence, due to the nonlinear nature of the estimation process, an alternating least-squares scheme iteratively solves the approximation problem. Our proposal searches for the samples of the instrument response with a global perspective, and the scaling coefficients of the basis functions locally at each spatial point. First, the iterative methodology relies on a least-squares solution for the instrument response, and quadratic programming for the scaling coefficients applied just to a subset of the measured fluorescence decays to initially estimate the instrument response to speed up the convergence. After convergence, the final stage computes the fluorescence impulse response at all spatial points. A comprehensive validation stage considers synthetic and experimental FLIM datasets of ex vivo atherosclerotic plaques and human breast cancer cell samples that highlight the advantages of the proposed BDE algorithm under different noise and initial conditions in the iterative scheme and parameters of the proposal.

Topology-Sensitive epidemic algorithm for information spreading in large-scale systems

Epidemic algorithms are an emerging technique that has recently gained popularity as a potentially effective solution for disseminating information in large-scale network systems. For some application scenarios, efficient and reliable data dissemination to all or a group of nodes in the network is necessary to provide with the communication services within the system. These studies may have a large impact in communication networks where epidemic-like protocols become a practice for message delivery, collaborative peer-to-peer applications, distributed database systems, routing in Mobile Ad Hoc networks, etc. In this paper we present, through various simulations, that an epidemic spreading process can be highly influenced by the network topology. We also provide a comparative performance analysis of some global parameters performance such as network diameter and degree of connectivity. Based on this analysis, we propose a new epidemic strategy that takes into account the topological structure in the network. The results show that the proposed epidemic algorithm outperform a classical timestamped anti-entropy epidemic algorithm in terms of the number of sessions required to reach a consistent state in the network system.

Iterative estimation of the number of autofluorescence components in a biological sample

This work is part of a continuous effort to achieve characterization of tissue from auto-fluorescence measurements. One particular problem is the estimation of the number of components in a sample from multi-spectral Fluorescence Lifetime Imaging Data (m-FLIM). The proposed method is based on a two-step iterative procedure, where first a blind end-member and abundance extraction algorithm is employed, followed by an evaluation of the resulting end-members by solving an optimal approximation problem. A threshold method is employed to evaluate if the extracted end-members are nonredundant. The validation of the proposal is performed by 3 m-FLIM data sets from post-mortem human coronary artery samples, where the results obtained matched the qualitative description provided by histopathology slides.

Particle filter for spectral unmixing

The paper addresses the problem of identification of fluorescent molecules, or fluorophores, in biological samples obtained from time-resolved fluorescence microscopy. The contribution of this work is an algorithm, based on particle filter optimization, that solves the spectral unmixing problem for more than two fluorophores even when they present highly overlapping spectra at different wavelengths. The estimation of the proportional contributions of the elements in the sample is carried out by a Bayesianmethodology. Validation is done by using synthetic mixtures with two components and ex-vivo samples from human atherosclerotic tissue.