Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on - (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region - one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees / severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.

New methods to measure and quantify tissue molecular composition and metabolism are a major driver of discovery in basic and clinical research. Optical methods are well suited for this task based on the non-invasive nature of many imaging and spectroscopy techniques, the variety of exogenous fluorescent probes available, and the ability to utilize label-free endogenous absorption signatures of tissue chromophores including oxy- and deoxy-hemoglobin, water, lipid, collagen, and glucose. Despite significant advances in biomedical imaging, there remain challenges in probing tissue information in a fast, wide-field, and non-invasive manner. Moreover, quantitative in vivo mapping of endogenous biomarkers such as water and lipids remain relatively less explored by the biomedical optics community due to their characteristic extinction spectra, which have distinct spectral features in the shortwave infrared, a wavelength band that has been traditionally more challenging to measure. The work presented in this dissertation was focused on developing instrumentation and algorithms for non-invasive quantification of tissue optical properties, fluorophore concentrations, and chromophore concentrations in a wide-field imaging format. All of the imaging methods and algorithms developed in this thesis extend the capability of the emerging technique called Spatial Frequency Domain Imaging (SFDI). First, a new imaging technique based on SFDI is presented that can quantify the quantum yield of exogenous fluorophores in tissue. This technique can potentially provide a new non-invasive means for in vivo mapping of local tissue environment such as temperature and pH. Next, an angle correction algorithm was developed for SFDI for more accurate estimation of tissue optical properties as well as chromophore concentrations in highly curved tissue, including small animal tumor models. Next, a wide-field label-free optical imaging system was developed to simultaneously measure water and lipids using the shortwave infrared (SWIR) wavelength region. Last, to break the bottleneck of processing speed in optical property inversion, new deep learning based models were developed to provide over 300× processing speed improvement. Together, these projects substantially extend the available contrasts and throughput of SFDI, providing opportunities for new preclinical and clinical applications. / 2020-10-22T00:00:00Z

La tomographie optique diffuse (TOD) est une technique d'imagerie médicale émergente utilisant la lumière proche infrarouge pour sonder les tissus biologiques. A partir de mesures non-invasives, cette technique permet d'obtenir les cartes en trois dimensions des coefficients d'absorption et de diffusion à l'intérieur des organes. Avec une approche multi-spectrale, la distribution spatiale des chromophores endogènes (hémoglobine, eau) peut aussi être obtenue. Pour certaines applications cliniques, il est souhaitable d'effectuer les mesures de TOD avec une sonde compacte qui regroupe tous les couples source-détecteur. Cependant, dans cette configuration, la sensibilité en profondeur est un défi majeur. Dans le cadre de cette thèse, nous proposons d'adresser ce challenge en utilisant des mesures résolues en temps. Une approche résolue en temps est développée pour optimiser la TOD dans le cas des mesures de réflectance à faibles distances source-détecteur. Cette approche inclut des aspects méthodologiques concernant le traitement des mesures résolues en temps par des algorithmes de TOD basés sur la transformée de Mellin-Laplace. Cette approche comporte aussi un volet instrumental qui consiste à optimiser la chaîne de détection sur deux points précis pour améliorer la détection et la localisation de contraste d'absorption en profondeur dans les milieux diffusants. Tout d'abord, l'impact de la réponse temporelle du détecteur est étudié avec des détecteurs de photons uniques disponibles dans le commerce (photomultiplicateurs classiques et hybrides). Dans un second temps, l'augmentation de la profondeur sondée avec de nouveaux détecteurs de photons uniques, les fast-gated single-photon avalanche diodes, est explorée au cours d'une collaboration avec le Politecnico de Milan. Pour finir, une étude illustre les performances de l'approche proposée en termes de résolution spatiale en profondeur pour différents arrangements des sources et détecteurs dans une sonde optique. Des sondes optiques dont la largeur est limitée à quelques centimètres ouvrent la voie à de nouvelles applications cliniques pour la TOD. Ces sondes peuvent accéder à des organes internes comme la prostate ou faciliter les examens médicaux sur des organes externes comme le sein ou le cerveau. / Diffuse optical tomography (DOT) is an emerging medical imaging technique using near-infrared light to probe biological tissues. This technique can retrieve three-dimensional maps of absorption and scattering coefficients inside organs from non-invasive measurements. With a multispectral approach, the spatial distribution of endogenous chromophores (hemoglobin, water) can even be obtained. For some clinical applications, it is desirable to carry out the measurements for DOT with a compact probe including all sources and detectors. However, the depth sensitivity is a real challenge in this configuration. We propose to tackle this challenge by using time-resolved measurements. A time-resolved approach is developed to perform DOT with reflectance measurements at short source-detector separation. This approach involves methodological aspects including the processing of time-resolved signals by DOT algorithms based on the Mellin-Laplace transform. Then, this approach consists in optimizing the detection chain on two aspects for enhancing the detection and localization of absorption contrast in depth in diffusive media. First, the impact of the temporal response of the detector is studied with commercially available single-photon detectors (classical and hybrid photomultipliers). Second, the enhancements in probed depth permitted with fast-gated single-photon avalanche diodes are explored in a joint work with the Politecnico di Milano. To finish, a study is carried out to illustrate the performance of the proposed approach with respect to spatial resolution in depth for different configurations of sources and detectors in the optical probe. Probes with a width limited to a few centimeters open the gate to multiple clinical interests. They could access intern organs like the prostate or facilitate the measurements on extern organs like the breast or the brain.

<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>