Le vieillissement est à l'origine de pathologies dermatologiques diverses favorisées par des facteurs extrinsèques, principalement les UV : cancers cutanés, dermatoporose, ulcères, escarres … Parmi eux, les ulcères sont source de douleurs chroniques, hospitalisations, majoration des coûts de santé et décès dans la population âgées. Or, le lien entre vieillissement cutané, vieillissement microvasculaire et ulcères n'est actuellement pas établi. Notre objectif est d'évaluer la dysfonction endothéliale liée au vieillissement sur les sites privilégiés d'ulcères, après développement des techniques d'évaluation de la fonction microvasculaire cutanée chez des sujets jeunes. La première partie de notre travail s'intéresse aux méthodes les plus récentes de mesure du flux sanguin cutané. Elle vise à comparer le Laser Speckle Contrast Imaging et le Laser Doppler Imaging en termes de reproductibilité puis à évaluer la corrélation des mesures selon ces deux techniques. La seconde partie est une étude méthodologique concernant la microdialyse, qui a pour objectif de comparer l'insertion des fibres au niveau intra-dermique ou sous-dermique, ainsi que d'évaluer leur pertinence dans la mesure de l'hyperhémie post-occlusive et par chauffage local. Dans la troisième partie, nous étudions le vieillissement physiologique de la fonction endothéliale de la zone périmalléolaire, zone à risque d'ulcération chez le sujet âgé. D'autres études seront nécessaires pour évaluer si la dysfonction microvasculaire liée au vieillissement peut favoriser la survenue d'ulcères et en préciser les mécanismes. / Aging is the cause of different dermatological diseases, facilitated by extrinsic factors, especially UV radiation: skin cancers, dermatoporosis, ulcers … Chronic ulcerations cause chronic pains, hospitalizations, increasing cost of health, death in the elderly population. Yet, the link between skin aging, microvascular aging and ulcers remains still unclear. Our objective is to study microvascular function on the gaiter skin area of elderly, because abnormal microvascular reactivity could contribute to ulcerations in this region, after development of the techniques used to assess cutaneous microvascular reactivity to young subjects. The first part of our work is interested in the most recent methods of measure of the cutaneous blood flow. It aims at comparing the Laser Speckle Contrast Imaging and the Laser Doppler Imaging in terms of reproducibility then at estimating the correlation of the measures according to these two techniques. The second part is a methodological study concerning the microdialysis, to determine wether intradermal or subdermal microdialysis fibers insertion can be used to assess post-occlusive hyperemia and local thermal hyperemia. In the third part, we study the physiological aging of in the supramedial malleolar skin, high-risk area of ulceration to the elderly subject. Further studies are required to determinate whether the changes in the physiological microvascular responses to local heating explain the higher incidence of skin ulcerations in the elderly.

This dissertation presents the development and use of a novel optical imaging system capable of monitoring changes in blood flow, oxygenated hemoglobin, deoxygenated hemoglobin, and absolute pO₂ in the brain. There are several imaging modalities capable of monitoring these parameters separately. Laser speckle contrast imaging (LSCI) and multi-spectral reflectance imaging (MSRI) have been used to monitor relative blood flow and hemoglobin changes respectively. Phosphorescence quenching, while not typically used for imaging, is capable of noninvasive measurements of pO₂. Combining these three techniques has led to the development of an imaging system that could ultimately lead to a better understanding of brain physiology. By combining techniques such as LSCI and MSRI, it becomes possible to estimate the cerebral metabolic rate of oxygen (CMRO₂), an important indicator of neuronal function. It is equally important to understand absolute pO₂ levels so that oxygen metabolism can be examined in context. Integrating phosphorescence quenching and a spatial light modulator into the imaging system allowed absolute pO₂ to be simultaneously measured in distinct regions. This new combined system was used to investigate pathophysiological conditions such as cortical spreading depression (CSD) and ischemia. The observed hemodynamic changes associated with these events were largely dictated by baseline oxygen levels and varied significantly in different regions. This finding highlighted the importance of having a system capable of monitoring hemodynamic changes and absolute pO₂ simultaneously while maintaining enough spatial resolution to distinguish the changes in different regions. It was found that animals with low baseline pO₂ were unable to deliver enough oxygen to the brain during events like CSD because of the high metabolic demand. In order for this technique to become more prevalent among researchers, it is essential to make it cost effective and simple to use. This was accomplished by replacing the expensive excitation sources with cheaper light emitting diodes (LEDs) and redesigning the software interface so that it was easier to control the entire device. The final system shows the potential to become a key tool for researchers studying the role of absolute pO₂ and other hemodynamic parameters during pathophysiological conditions such as CSD and ischemia. / text

Cerebral blood flow (CBF) measures are central to the investigation of ischemic strokes, spreading depressions, functional and neuronal activation. Laser Speckle Contrast Imaging (LSCI) is an optical imaging technique that has been used to obtain CBF measures in vivo at high spatial and temporal resolutions, by quantifying the localized spatial blurring of backscattered coherent light induced by blood flow. Despite being widely used for biomedical applications, LSCI's critical limitations such as its tendency to underestimate large flow changes and its inability to accurately estimate CBF through a thinned skull have not been overcome. This dissertation presents a new Multi Exposure Speckle Imaging (MESI) technique that combines a new instrument and mathematical model to overcome these limitations. Additionally, in a pilot clinical study, an adapted neurosurgical microscope was used to obtain intra-operative LSCI images of CBF in humans. The MESI instrument accurately estimates experimental constants by imaging backscattered speckles over a wide range of the camera's exposure durations. The MESI mathematical model helps account for light that has scattered from both static and moving particles. In controlled flow experiments using tissue simulating phantoms, the MESI technique was found to estimate large changes in flow accurately and the estimates of flow changes were found to be unaffected by the presence of static particles in these phantoms. In an in vivo experiment in which the middle cerebral artery in mice was occluded to induce ~100% reduction in CBF, not only was the reduction in CBF accurately estimated by the MESI technique but these estimates of CBF changes were found to be unaffected by the presence of a thinned skull. The validity of statistical models used to derive the MESI mathematical model was confirmed using in vivo dynamic light scattering (DLS) measurements of CBF in mice. The MESI technique's potential to estimate absolute values of CBF in vivo was demonstrated by comparing CBF estimates obtained using the MESI technique to DLS measurements. The MESI technique's ability to measure CBF changes quantitatively through a thinned skull makes it particularly useful in chronic and long term studies leading to the development of better, more accurate stroke models. / text

Laser speckle contrast imaging (LSCI) and its extension, multi-exposure laser speckle contrast imaging (MELSCI) are non-invasive techniques to monitor peripheral blood perfusion. One of the main drawbacks of LSCI and MELSCI in clinical use is that the techniques are sensitive to tissue movement. Moreover, the image background contributes to unnecessary data. The aim of this project was to develop and evaluate different methods to detect local motion artifacts and image backgrounds in LSCI and MELSCI. In this project, three different methods were developed: one using statistical analysis and two using machine learning. The method based on classical statistics was developed in MATLAB with a dataset made up of 1797 frames of 256 x 320 images taken from a recording of a hand where the thumb and middle finger were taking turns making small movements while the middle finger was the subject of three different states made by an occlusion cuff (baseline, occlusion, and reperfusion). The main filter that was used in the first method was the Hampel filter. Furthermore, networks for the machine learning method were developed in Python using the same dataset but with 20,000 small patches extracted from the dataset of sizes 3 x 3 to 21 x 21 pixels. The first machine learning method was based on two-dimensional data patches, hence no time dimension was included, while the second machine learning method used three-dimensional data patches where the time dimension was included (from 1s to 10s). The generation of ground truth for the dataset was manually created frame by frame in a ground truth generation graphical user interface (GUI) in MATLAB. To assess the three methods, the Dice coefficient was used. The statistical method resulted in a Dice coefficient of 0.7557. The highest Dice coefficient for the machine learning method with a 2D dataset was 0.2902 (patch size 13 x 13) and the lowest was 0.2372 (patch size 7 x 7). For the machine learning method with 3D datasets, the patch size of 21 x 21 x 4 resulted in the highest Dice coefficient (0.5173), and the 21 x 21 x 40 model had the lowest Dice coefficient (0.1782). Since the two methods based on temporal data proved to be performing best in this project, one conclusion for further development of an improved model is the usage of temporal data in the training of a model. However, one important difference between the statistical method and the three-dimensional machine learning method is that the statistical method does not handle fast perfusion changes as well as the machine learning method and can not detect image background and static tissue. Therefore, the overall most useful method to further develop is the three-dimensional machine learning method.