Le cancer de la peau est un enjeu majeur de santé publique. Il représente le type de cancer ayant le plus fort taux de prévalence et le nombre de cas semble être en constante augmentation. Aujourd'hui, la méthode de référence pour le diagnostic du cancer cutané nécessite un échantillon de tissu suspect, appelé biopsie, prélevé après un simple examen visuel de la peau du patient. Par conséquent, près de 60 % des biopsies se révèlent être bénignes et environ 20 % des cancers de la peau ne sont pas détectés.Les travaux de recherche présentés dans ce manuscrit de thèse portent sur le développement d'un dispositif de tomographie par cohérence optique confocale en ligne (LC-OCT) capable de produire des images d'une qualité similaire aux coupes histologiques de manière non invasive et in vivo. Le prototype conçu fonctionne à une longueur d'onde centrale autour de 800 nm avec une largeur spectrale d'environ 150 nm. Il a été appliqué à l'imagerie in vivo de la peau avec une résolution spatiale quasi-isotrope d'environ 1 µm et une profondeur de pénétration de 400 µm. Ce dispositif pourrait alors être utilisé pour améliorer l'efficacité du processus de diagnostic du cancer de la peau en limitant le nombre de cas non détectés ainsi que le nombre de biopsies inutiles.Nous présentons ensuite un dispositif de LC-OCT fonctionnant dans deux bandes spectrales centrées autour de 770 nm et 1250 nm. La première bande produit des images à haute résolution (1.3 µm x 1.2 µm, latéral x axial) tandis que la seconde offre une profondeur de pénétration accrue (700 µm). En fusionnant les images produites dans les deux bandes il a été possible de produire des images avec une bonne résolution en superficie tout en ayant une profondeur de pénétration étendue. De plus, acquérir des images d'un échantillon dans deux bandes spectrales différentes permet dans une certaine mesure d'obtenir des informations sur les propriétés spectrales de l'échantillon.Finalement, nous présentons une preuve de concept d'un dispositif de LC-OCT couplé avec un microscope Raman ainsi que quelques exemples d'application. La microscopie Raman est une modalité spectroscopique qui permet d'identifier des molécules et ainsi de mesurer "l'empreinte digitale" d'un échantillon. Cette modalité pourrait alors fournir des informations complémentaires aux images morphologiques acquises par LC-OCT à propos de la composition biomoléculaire de l'échantillon. / Skin cancer is a major public health issue. Among all types of cancer, skin cancer has the highest prevalence rate and the number of cases seems to be steadily increasing. Currently, the gold standard of skin cancer diagnosis requires a sample of suspicious tissue, called a biopsy, removed after a simple visual inspection of the patient's skin. Consequently, almost 60 % of biopsies result in benign diagnoses, and approximately 20 % of all skin cancers are missed.The research presented in this thesis revolves around the development of a line-field confocal optical coherence tomography (LC-OCT) device capable of producing non-invasive in vivo images similar in quality to histological cuts. The designed prototype operates at a center wavelength around 800 nm with a spectral width of approximately 150 nm. It has been applied to in vivo skin imaging with an almost isotropic spatial resolution of about 1 µm and a depth penetration reaching 400 µm. This device could thus be used to improve the efficiency of skin cancer diagnosis by limiting the number of undiagnosed cases and the number of unnecessary biopsies.We then present a LC-OCT device system operating in two spectral bands centered around 770 nm and 1250 nm. The first band produces high resolution images (1.3 µm x 1.2 µm, lateral x axial) while the second provides enhanced penetration depth (700 µm). By merging the images acquired in the two bands it has been possible to produce images with both high resolution and high penetration. Moreover, acquiring images of a sample in two different spectral bands can give, to a certain extent, information on the spectral properties of the sample.Lastly, we present a proof-of-concept LC-OCT prototype coupled together with a Raman microscope, as well as some application examples. Raman microscopy is a spectroscopic method capable of identifying molecules present in a sample and thus measuring the "fingerprint" of a sample. This modality could then provide complementary information to the morphological images provided by LC-OCT about the biomolecular composition of the sample.

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In this work, we use optical methods to study electrochemical reactions and processes occurring on the nanometer length scale. Optical methods are advantageous over traditional electrochemical methods because of their high spatial resolution and sensitivity at both the single nanoparticle and single molecule level. This dissertation describes a series of studies in which super-localization and dark-field optical imaging is used to provide insight into spatial and temporal heterogeneity in nanoscale electrochemical systems with <25 nm spatial resolution. In the first set of experiments, three-dimensional (3-D) super-resolution imaging is used to determine the tip-substrate distance in nanoscale scanning electrochemical microscopy (SECM) with precision better than 25 nm. Correlating the tip-substrate distance using both optical and electrochemical techniques showed excellent agreement. Second, single nanoparticles (NP) were delivered through a nanopipette, and their resistive-pulse signals were correlated with a fluorescence optical signal. The diffusion trajectories of individual NP delivered to the external solution and to an electrified interface were obtained by 3-D super-resolution imaging, and showed signatures of both sub-diffusive and super-diffusive behavior, depending on the balance of forces between the flow from the pipette and the applied potential at the electrified substrate. Next, we studied the influence of surface oxide layers on single silver NP electrodissolution by tracking the intensity and spatial variation of scattering from single nanoparticles over time. We discovered that silver NPs can undergo electrodissolution in either a spatially symmetric or asymmetric manner, based on the nature of the surface oxide layer. Moreover, we also reported the simultaneous electrodeposition of silver oxide at the electrode surface during the electrodissolution of silver NPs, which enabled us to study the effect of multiple simultaneous redox reactions and their effects on one another. Overall, these experiments reveal local heterogeneity in nanoscale electrochemical processes and allow for many single nanoparticles to be measured in parallel, revealing relationships that are hidden using traditional electrochemical measurements. / Chemistry

To reduce the risk of pancreatic fistula after pancreatectomy, a satisfactory blood flow at the pancreatic stump is considered crucial. Our group has developed and validated a real-time computational imaging analysis of tissue perfusion, using fluorescence imaging, the fluorescence-based enhanced reality (FLER). Hyperspectral imaging (HSI) is another emerging technology, which provides tissue-specific spectral signatures, allowing for perfusion quantification. Both imaging modalities were employed to estimate perfusion in a porcine model of partial pancreatic ischemia. Perfusion quantification was assessed using the metrics of both imaging modalities (slope of the time to reach maximum fluorescence intensity and tissue oxygen saturation (StO2), for FLER and HSI, respectively). We found that the HSI-StO2 and the FLER slope were statistically correlated using the Spearman analysis (R = 0.697; p = 0.013). Local capillary lactate values were statistically correlated to the HSI-StO2 and to the FLER slope (R = −0.88; p < 0.001 and R = −0.608; p = 0.0074). HSI-based and FLER-based lactate prediction models had statistically similar predictive abilities (p = 0.112). Both modalities are promising to assess real-time pancreatic perfusion. Clinical translation in human pancreatic surgery is currently underway.

Computational imaging has been playing an important role in understanding and analysing the captured images. Both image segmentation and restoration has been in-tegral parts of computational imaging. The studies performed in this thesis has been focussed toward developing novel algorithms for image segmentation and restoration. Study related to usage of Morozov Discrepancy Principle in Di use Optical Imaging was also presented here to show that hyper parameter selection could be performed with ease. The Laplacian of Gaussian (LoG) and Canny operators use Gaussian smoothing be-fore applying the derivative operator for edge detection in real images. The LoG kernel was based on second derivative and is highly sensitive to noise when compared to the Canny edge detector. A new edge detection kernel, called as Helmholtz of Gaussian (HoG), which provides higher di suavity is developed in this thesis and it was shown that it is more robust to noise. The formulation of the developed HoG kernel is similar to LoG. It was also shown both theoretically and experimentally that LoG is a special case of HoG. This kernel when used as an edge detector exhibited superior performance compared to LoG, Canny and wavelet based edge detector for the standard test cases both in one- and two-dimensions. The linear inverse problem encountered in restoration of blurred noisy images is typically solved via Tikhonov minimization. The outcome (restored image) of such min-imitation is highly dependent on the choice of regularization parameter. In the absence of prior information about the noise levels in the blurred image, ending this regular-inaction/hyper parameter in an automated way becomes extremely challenging. The available methods like Generalized Cross Validation (GCV) may not yield optimal re-salts in all cases. A novel method that relies on minimal residual method for ending the regularization parameter automatically was proposed here and was systematically compared with the GCV method. It was shown that the proposed method performance was superior to the GCV method in providing high quality restored images in cases where the noise levels are high Di use optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. An automated method for selection of regularization/hyper parameter that incorporates Morozov discrepancy principle(MDP) into the Tikhonov method was proposed and shown to be a promising method for the dynamic Di use Optical Tomography.

Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro- vides functional information of the soft biological tissues in-vivo, such as breast and brain tissues. The near infrared (NIR) light (600-1000 nm) is the interrogating radiation, which is typically delivered and collected using fiber bundles placed on the boundary of the tissue. The internal optical property distribution is estimated via model-based image reconstruction algorithm using these limited boundary measurements. Image reconstruction problem in DOT is known to be non-linear, ill-posed, and some times under-determined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of the regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of non-quadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases were obtained automatically using the generalized cross-validation (GCV) method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that while quadratic penalty may be able to provide better separation between two closely spaced targets, it's contrast recovery capability is limited and the sparseness promoting penalties, such as `1, Cauchy, Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets with Geman-McClure penalty being the most optimal one. Effective usage of image guidance by incorporating the refractive index (RI) variation in computational modeling of light propagation in tissue is investigated to assess its impact on optical-property estimation. With the aid of realistic patient breast three-dimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in image-guided diffuse optical tomography (IG-DOT) using numerical simulations. It is also shown that by assuming identical RI for all regions of tissue would lead to erroneous estimation of optical properties. The a priori knowledge of the RI for the segmented regions of tissue in IG-DOT, which is difficult to obtain for the in vivo cases, leads to more accurate estimates of optical properties. Even inclusion of approximated RI values, obtained from the literature, for the regions of tissue resulted in better estimates of optical properties, with values comparable to that of having the correct knowledge of RI for different regions of tissue. Image reconstruction in IG-DOT procedure involves reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem to be well-determined compared to traditional under- determined case. Still, the methods that are deployed in this case are same as the one used for traditional diffuse optical image reconstruction, which involves regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method was proposed here to perform the image reconstruction procedure and shown to be providing solutions that are closely matching with ones obtained using established methods. The proposed method also has the distinctive advantage of being more efficient due to being regularization free, involving only repeated forward calculations.

Diffuse Optical Tomography is a diagnostic imaging modality where optical parameters such as absorption coefficient, scattering coefficient and refractive index distributions are recovered to form the internal tissue metabolic image. Near-infrared (NIR) light has the potential to be used as a noninvasive means of diagnostic imaging within the human breast. Due to the diffusive nature of light in tissue, computational model-based methods are required for functional imaging. The main goal is to recover the spatial variation of optical properties which shed light on the different metabolic states of tissue and tissue like media. This thesis addresses the issue of quantitative recovery of optical properties of tissue-mimicking phantom and pork tissue using diffuse optical tomography (DOT). The main contribution of the present work is the development of robust, efficient and fast optical property reconstruction algorithms for a direct 3-D DOT imaging system. There are both theoretical and experimental contributions towards the development of an imaging system and procedures to minimize accurate data collection time, overall system automation as well as development of computational algorithms. In nurturing the idea of imaging using NIR light into a fully developed direct 3-D imaging system, challenges from the theoretical and computational aspects have to be met. The recovery of the optical property distribution in the interior of the object from the often noisy boundary measurements on light, is an ill-posed ( and nonlinear) problem. This is particularly true, when one is interested in a direct 3-D image reconstruction instead of the often employed stacking of 2-D cross-sections obtained from solving a set of 2-D DOT problems. In order to render the DOT, a useful diagnostic imaging tool and a robust reconstruction procedure giving accurate and reliable parameter recovery in the scenario, where the number of unknowns far outnumbers the number of independent data sets that can be gathered (for example, the direct 3-D recovery mentioned earlier) is essential. Here, the inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. An interesting development in this direction has been the development of Broyden’ s and adjoint Broyden’ s methods that avoids direct Jacobian computation in each iteration thereby making the full 3-D a reality. Conventional model based iterative image reconstruction (MoBIIR) algorithm uses Newton’ s and it’s variant methods, where it required repeated evaluation of whole Jacobian, which consumes bulk time in reconstruction process. The explicit secant and adjoint information based fast 2-D/3-D image reconstruction algorithms without repeated evaluation of the Jacobian is proposed in diffuse optical tomography, where the computational time has been decreased many folds by updating the Jacobian successively through low rank update. An alternative route to the iterative solution is attempted by introducing an artificial dynamics in the system and treating the steady-state response of the artificially evolving dynamical system as a solution. The objective is to consider a novel family of pseudo-dynamical 2-D and 3-D systems whose numerical integration in time provides an asymptotic solution to the inverse problem at hand. We convert Gauss-Newton’ s equation for updates into a pseudo-dynamical (PD) form by explicitly adding a time derivative term. As the pseudo-time integration schemes do not need such explicit matrix inversion and depending on the pseudo-time step size, provides for a layer of regularization that in turn helps in superior quality of 2-D and 3-D image reconstruction. A cost effective frequency domain Matlab based 2-D/3-D automated imaging system is designed and built. The complete instrumentation (including PC-based control software) has been developed using a single modulated laser source (wavelength 830nm) and a photo-multiplier tube (PMT). The source and detector fiber change their positions dynamically allowing us to gather data at multiple source and detector locations. The fiber positions are adjusted on the phantom surface automatically for scanning variable size phantoms. A heterodyning scheme was used for reading out the measurement using a lock-in-amplifier. The Matlab program carries out sequence of actions such as instrument control, data acquisition, data organization, data calibration and reconstruction of image. The Gauss-Newton’ s, Broyden’ s, adjoint Broyden’ s and pseudo-time integration algorithms are evaluated using the simulation data as well as data from the experimental DOT system. Validation of the system and the reconstruction algorithms were carried out on a real tissue, a pork tissue with an embedded fat inhomogeneity. The results were found to match the known parameters closely.

Les systèmes moléculaires à base de cations lanthanides luminescents possèdent des propriétés photophysiques très intéressantes pour les applications en bioimagerie optique (résistance au photoblanchiment, bandes d’émission fines, temps de vie de luminescence longs). La lumière émise peut couvrir une large gamme spectrale, selon le cation lanthanide choisi. Cependant, les lanthanides ont des coefficients d’extinction faibles. Il est donc nécessaire d’utiliser une voie de sensibilisation indirecte - l’effet d’antenne, avec un chromophore placé à proximité du lanthanide. De plus, afin d’amplifier le signal émis, plusieurs cations métalliques peuvent être introduits dans la molécule. Dans cette thèse, nous avons cherché à développer de nouveaux agents potentiels d’imagerie à base de systèmes polynucléaires de lanthanides. Notre objectif a été de programmer l’assemblage de complexes supramoléculaires tridimensionnels et d’améliorer les propriétés optiques pour les applications biologiques. Pour ce faire, nous avons choisi d’employer des ligands tripodaux composés d’une ancre centrale triaminotriptycène et de trois bras possédant les sites coordinants. Ces ligands ont ensuite été modifiés de façon à optimiser les propriétés physico-chimiques. Par ailleurs, la synthèse de nouvelles plateformes centrales a été également effectuée afin de poursuivre leur développement dans le futur. La synthèse et la caractérisation de ligands tripodaux L1 et L2 et de leurs complexes avec des lanthanides ont été réalisées. Des études détaillées de spéciations avec les ligands L1 et L2 ont permis de démontrer la stabilité thermodynamique des systèmes tétranucléaires formés en préférence avec les lanthanides légers. Des études spectroscopiques ont caractérisé leur luminescence dans le visible et dans le proche infrarouge. D’autre part, nous avons préparé et caractérisé des nouveaux composés en combinant un chromophore porphyrinique et des récepteurs complexant les lanthanides. Ces sondes bimodales peuvent trouver une application dans l’imagerie et la thérapie photodynamique. / Molecular systems based on luminescent lanthanide cations have very valuable photo-physical properties for applications in optical bioimaging (resistance to photobleaching, sharp emission bands, and longue luminescence lifetimes). The emitted light can cover a wide spectral range, according to the chosen lanthanide cation. However, the lanthanides have low extinction coefficients and must be sensitized indirectly via the antenna effect by using a chromophore located in proximity. The luminescence intensity per molecule can be also increased by accommodating several lanthanides within a single compound. In this thesis, we report on the development of new potential imaging agents based on polynuclear systems with lanthanides. The main goal was to program supramolecular well-defined tridimensional complexes in order to enhance their optical properties for biological applications. To do this, we have chosen to employ tripodal ligands composed of a central triaminotriptycene anchor and three arms with coordinating sites. Moreover, these ligands have been functionalized to improve their physicochemical properties. In view of future developments, the synthesis of new central platforms was also performed. The synthesis and characterization of tripodal ligands L1 and L2 and their complexes with lanthanides were carried out. Detailed speciation studies with L1 and L2 show a high thermodynamic stability of tetranuclear systems formed preferably with lighter lanthanides. In addition, spectroscopic studies reveal that the sensitization of luminescence is achieved in the visible as well as in the near infrared. Furthermore, we have prepared and characterized novel compounds by combining a porphyrin chromophore and a receptor for lanthanide complexation. These bimodal probes may find applications in imaging and in photodynamic therapy.