Algorithms for Fluorescence Lifetime Microscopy and Optical Coherence Tomography Data Analysis: Applications for Diagnosis of Atherosclerosis and Oral Cancer

With significant progress made in the design and instrumentation of optical imaging systems, it is now possible to perform high-resolution tissue imaging in near real-time. The prohibitively large amount of data obtained from such high-speed imaging systems precludes the possibility of manual data analysis by an expert. The paucity of algorithms for automated data analysis has been a major roadblock in both evaluating and harnessing the full potential of optical imaging modalities for diagnostic applications. This consideration forms the central theme of the research presented in this dissertation. Specifically, we investigate the potential of automated analysis of data acquired from a multimodal imaging system that combines fluorescence lifetime imaging (FLIM) with optical coherence tomography (OCT), for the diagnosis of atherosclerosis and oral cancer. FLIM is a fluorescence imaging technique that is capable of providing information about auto fluorescent tissue biomolecules. OCT on the other hand, is a structural imaging modality that exploits the intrinsic reflectivity of tissue samples to provide high resolution 3-D tomographic images. Since FLIM and OCT provide complimentary information about tissue biochemistry and structure, respectively, we hypothesize that the combined information from the multimodal system would increase the sensitivity and specificity for the diagnosis of atherosclerosis and oral cancer. The research presented in this dissertation can be divided into two main parts. The first part concerns the development and applications of algorithms for providing quantitative description of FLIM and OCT images. The quantitative FLIM and OCT features obtained in the first part of the research, are subsequently used to perform automated tissue diagnosis based on statistical classification models. The results of the research presented in this dissertation show the feasibility of using automated algorithms for FLIM and OCT data analysis for performing tissue diagnosis

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence

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

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

An astrobiological study of high latitude martian analogue environments

The search for life on Mars is in part reliant on the understanding of Martian environments, both past and present, in terms of what life may inhabit these environments, how this life may be preserved in the rock record, and how this rock record may be detected during future missions to Mars. In particular, the upcoming European Space Agency mission ‘ExoMars’ has the primary aim to identify evidence of past or present life on Mars, and the work presented here is carried out within this context. Volcanism is a geological process common to both Earth and Mars, and this work sought to conduct a multidisciplinary astrobiological study of terrestrial volcanic and associated hydrothermal environments that exist geographically at high latitudes. Specifically, subglacial basaltic volcanic environments were explored in terms of phylogenetic diversity, preservation of biosignatures, and habitability under Martian conditions. Additionally, these and other volcanic environments were utilised in the development and testing of the Panoramic Camera – an instrument that will form an integral component of the ExoMars rover instrument suite. Results presented within this thesis demonstrate that subglacially erupted lavas provide a habitat for a diverse bacterial community, and that when such a community is subject to present-day Martian analogue conditions, survivability is significantly enhanced when a simulated subglacial volcanic system (i.e. heat and ice) is present. However, the generation of bioalteration textures – a biosignature common to glassy basaltic lavas – appears to be less common in subglacially-erupted lavas than their oceanic counterparts. Lastly, this work demonstrates the ability of the ExoMars PanCam in the detection of astrobiological targets, and shows the importance of utilising Martian analogue terrains both for biological studies, and also for testing rover instrumentation in preparation for upcoming missions

Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

Fluorescence Methods for Investigation of Living Cells and Microorganisms

Fluorescence methods play a leading role in the investigation of biological objects. They are the only non-destructive methods for investigating living cells and microorganisms in vivo. Using intrinsic and artificial fluorescence methods provides deep insight into mechanisms underlying physiological and biochemical processes. This book covers a wide range of modern methods involved in experimental biology. It illustrates the use of fluorescence microscopy and spectroscopy, confocal laser scanning microscopy, flow cytometry, delayed fluorescence, pulse-amplitude-modulation fluorometry, and fluorescent dye staining protocols. This book provides an overview of practical and theoretical aspects of fluorescence methods and their successful application in the investigation of static and dynamic processes in living cells and microorganisms

Fundamental aspects of imaging matrix assisted laser desorption ionisation mass spectrometry.

Matrix assisted laser desorption/ionisation mass spectrometry imaging is a recent addition to the existing family of molecular imaging technologies. It has the capacity to map the distribution of molecules within a biological tissue section, without the need for radionuclide or fluorescent labelling procedures. The primary aim of the work presented in this thesis was to assess the use of a high repetition rate laser for MALDI-MS image analysis by developing methodologies for the detection of a number of different compounds from a variety of biological tissues. Additional investigations include and examination of strategies for normalisation and statistical interpretation of MALDI-MS image data. The application of a solvent assisted indirect imaging approach for the analysis of drugs in skin is described. Studies have been carried out in order to gauge how the use of a solvent in the blotting process aids the indirect imaging technique. Further experiments have been performed to assess the level of analyte migration induced by incorporation of a sample wetting step. In a direct tissue imaging experiment the distribution of a prodrug and its active metabolite has been determined in treated tumour tissue. Endogenous markers have been employed to assist in determining correlation between drug activation and hypoxic regions within tumours. Different methods of data normalisation are investigated for their effects on image data, and statistical evaluation of MALDI-MS acquired image data have been examined in relation to extracting hidden variables from multidimensional image data sets