Statistical modeling and processing of high frequency ultrasound images: application to dermatologic oncology

Cette thèse étudie le traitement statistique des images d’ultrasons de haute fréquence, avec application à l’exploration in-vivo de la peau humaine et l’évaluation non invasive de lésions. Des méthodes Bayésiennes sont considérées pour la segmentation d’images échographiques de la peau. On y établit que les ultrasons rétrodiffusés par la peau convergent vers un processus aléatoire complexe de type Levy-Flight, avec des statistiques non Gaussiennes alpha-stables. L’enveloppe du signal suit une distribution Rayleigh généralisée à queue lourde. A partir de ces résultats, il est proposé de modéliser l’image ultrason de multiples tissus comme un mélange spatialement cohérent de lois Rayleigh à queues lourdes. La cohérence spatiale inhérente aux tissus biologiques est modélisée par un champ aléatoire de Potts-Markov pour représenter la dépendance locale entre les composantes du mélange. Un algorithme Bayésien original combiné à une méthode Monte Carlo par chaine de Markov (MCMC) est proposé pour conjointement estimer les paramètres du modèle et classifier chaque voxel dans un tissu. L’approche proposée est appliquée avec succès à la segmentation de tumeurs de la peau in-vivo dans des images d’ultrasons de haute fréquence en 2D et 3D. Cette méthode est ensuite étendue en incluant l’estimation du paramètre B de régularisation du champ de Potts dans la chaine MCMC. Les méthodes MCMC classiques ne sont pas directement applicables à ce problème car la vraisemblance du champ de Potts ne peut pas être évaluée. Ce problème difficile est traité en adoptant un algorithme Metropolis-Hastings “sans vraisemblance” fondé sur la statistique suffisante du Potts. La méthode de segmentation non supervisée, ainsi développée, est appliquée avec succès à des images échographiques 3D. Finalement, le problème du calcul de la borne de Cramer-Rao (CRB) du paramètre B est étudié. Cette borne dépend des dérivées de la constante de normalisation du modèle de Potts, dont le calcul est infaisable. Ce problème est résolu en proposant un algorithme Monte Carlo original, qui est appliqué avec succès au calcul de la borne CRB des modèles d’Ising et de Potts. ABSTRACT : This thesis studies statistical image processing of high frequency ultrasound imaging, with application to in-vivo exploration of human skin and noninvasive lesion assessment. More precisely, Bayesian methods are considered in order to perform tissue segmentation in ultrasound images of skin. It is established that ultrasound signals backscattered from skin tissues converge to a complex Levy Flight random process with non-Gaussian _-stable statistics. The envelope signal follows a generalized (heavy-tailed) Rayleigh distribution. Based on these results, it is proposed to model the distribution of multiple-tissue ultrasound images as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by a Potts Markov random field. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. The proposed method is successfully applied to the segmentation of in-vivo skin tumors in high frequency 2D and 3D ultrasound images. This method is subsequently extended by including the estimation of the Potts regularization parameter B within the Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because the likelihood of B is intractable. This difficulty is addressed by using a likelihood-free Metropolis-Hastings algorithm based on the sufficient statistic of the Potts model. The resulting unsupervised segmentation method is successfully applied to tridimensional ultrasound images. Finally, the problem of computing the Cramer-Rao bound (CRB) of B is studied. The CRB depends on the derivatives of the intractable normalizing constant of the Potts model. This is resolved by proposing an original Monte Carlo algorithm, which is successfully applied to compute the CRB of the Ising and Potts models

First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Neutro-Connectedness Theory, Algorithms and Applications

Connectedness is an important topological property and has been widely studied in digital topology. However, three main challenges exist in applying connectedness to solve real world problems: (1) the definitions of connectedness based on the classic and fuzzy logic cannot model the “hidden factors” that could influence our decision-making; (2) these definitions are too general to be applied to solve complex problem; and (4) many measurements of connectedness are heavily dependent on the shape (spatial distribution of vertices) of the graph and violate the intuitive idea of connectedness. This research focused on solving these challenges by redesigning the connectedness theory, developing fast algorithms for connectedness computation, and applying the newly proposed theory and algorithms to solve challenges in real problems. The newly proposed Neutro-Connectedness (NC) generalizes the conventional definitions of connectedness and can model uncertainty and describe the part and the whole relationship. By applying the dynamic programming strategy, a fast algorithm was proposed to calculate NC for general dataset. It is not just calculating NC map, and the output NC forest can discover a dataset’s topological structure regarding connectedness. In the first application, interactive image segmentation, two approaches were proposed to solve the two most difficult challenges: user interaction-dependence and intense interaction. The first approach, named NC-Cut, models global topologic property among image regions and reduces the dependence of segmentation performance on the appearance models generated by user interactions. It is less sensitive to the initial region of interest (ROI) than four state-of-the-art ROI-based methods. The second approach, named EISeg, provides user with visual clues to guide the interacting process based on NC. It reduces user interaction greatly by guiding user to where interacting can produce the best segmentation results. In the second application, NC was utilized to solve the challenge of weak boundary problem in breast ultrasound image segmentation. The approach can model the indeterminacy resulted from weak boundaries better than fuzzy connectedness, and achieved more accurate and robust result on our dataset with 131 breast tumor cases

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Multispectral imaging for preclinical assessment of rheumatoid arthritis models

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune condition affecting multiple body systems. Murine models of RA are vital in progressing understanding of the disease. The severity of arthritis symptoms is currently assessed in vivo by observations and subjective scoring which are time-consuming and prone to bias and inaccuracy. The main aim of this thesis is to determine whether multispectral imaging of murine arthritis models has the potential to assess the severity of arthritis symptoms in vivo in an objective manner. Given that pathology can influence the optical properties of a tissue, changes may be detectable in the spectral response. Monte Carlo modelling of reflectance and transmittance for varying levels of blood volume fraction, blood oxygen saturation, and water percentage in the mouse paw tissue demonstrated spectral changes consistent with the reported/published physiological markers of arthritis. Subsequent reflectance and transmittance in vivo spectroscopy of the hind paw successfully detected significant spectral differences between normal and arthritic mice. Using a novel non-contact imaging system, multispectral reflectance and transmittance images were simultaneously collected, enabling investigation of arthritis symptoms at different anatomical paw locations. In a blind experiment, Principal Component (PC) analysis of four regions of the paw was successful in identifying all 6 arthritic mice in a total sample of 10. The first PC scores for the TNF dARE arthritis model were found to correlate significantly with bone erosion ratio results from microCT, histology scoring, and the manual scoring method. In a longitudinal study at 5, 7 and 9 weeks the PC scores identified changes in spectral responses at an early stage in arthritis development for the TNF dARE model, before clinical signs were manifest. Comparison of the multispectral image data with the Monte Carlo simulations suggest that in this study decreased oxygen saturation is likely to be the most significant factor differentiating arthritic mice from their normal littermates. The results of the experiments are indicative that multispectral imaging performs well as an assessor of arthritis for RA models and may outperform existing techniques. This has implications for better assessment of preclinical arthritis and hence for better experimental outcomes and improvement of animal welfare

Development and evaluation of low-dose rate radioactive gold nanoparticles for application in nanobrachytherapy

Depuis les dix dernières années, l’innovation des traitements d’oncologie a fait une utilisation croissante de la nanotechnologie. De nouveaux traitements à base de nanoparticules (NPs) sont notamment rendus au stade de l’essai clinique. Possédant des caractéristiques physico-chimiques particulières, les NPs peuvent être utilisées afin de bonifier l’effet thérapeutique des traitements actuels. Par exemple, l’amélioration de la curiethérapie (c.-à-d. radiothérapie interne) nécessite le développement de nouvelles procédures permettant de diminuer la taille des implants, et ce, tout en augmentant l’homogénéité de la dose déposée dans les tumeurs. Des études théoriques et expérimentales ont démontré que l’injection de NPs d’or à proximité des implants traditionnels de curiethérapie de faible débit de dose (par ex. 125I, 103Pd) permettrait d’augmenter significativement leur efficacité thérapeutique. L'interaction entre l’or et les photons émis par les implants de curiethérapie (c.-à-d. l’effet de radiosensibilisation) génère des rayonnements divers (photoélectrons, électrons Auger, rayons X caractéristiques) qui augmentent significativement la dose administrée. Dans le cadre de cette thèse, l’approche proposée était de développer des NPs d’or radioactives comme nouveau traitement de curiethérapie contre le cancer de la prostate. L’aspect novateur et unique était de synthétiser une particule coeurcoquille (Pd@Au) en utilisant l’isotope actuellement employé en curiethérapie de la prostate: le palladium-103 (103Pd, 20 keV). Dans ce cas-ci, la présence d’atomes d’or permet de produire l’effet de radiosensibilisation et d’augmenter la dose déposée. La preuve de concept a été démontrée par la synthèse et la caractérisation des NPs 103Pd@Au-PEG NPs. Ensuite, une étude longitudinale in vivo impliquant l’injection des NPs dans un modèle xénogreffe de tumeurs de la prostate chez la souris a été effectuée. L’efficacité thérapeutique induite par les NPs a été démontrée par le retard de la croissance tumorale des souris injectées par rapport aux souris non injectées (contrôles). Enfin, une étude de cartographie de la dose générée par les NPs à l’échelle cellulaire et tumorale a permis de comprendre davantage les mécanismes thérapeutiques liés aux NPs radioactives. En résumé, l’ensemble des travaux présentés dans cette thèse font office de précurseurs relativement au domaine de la nanocuriethérapie, et pourraient ouvrir la voie à une nouvelle génération de NPs pour la radiothérapie.The last decade saw the emergence of new innovative oncology treatments based on nanotechnology. New treatments using nanoparticles (NPs) are now translated to clinical trials. NPs possess unique physical and chemical properties that can be advantageously used to improve the therapeutic effect of current treatments. For instance, therapeutic efficiency enhancement related to internal radiotherapy (i.e., brachytherapy), requires the development of new procedures leading to a decrease of the implant size, while increasing the dose homogeneity and distribution in tumors. Several theoretical and experimental studies based on low-dose brachytherapy seeds (e.g., 125I and 103Pd) combined with gold nanoparticles (Au NPs) showed very promising results in terms of dose enhancement. Gold is a radiosensitizer that enhances the efficiency of radiotherapy by increasing the energy deposition in the surrounding tissues. Dose enhancement is caused by the photoelectric products (photoelectrons, Auger electrons, characteristic X-rays) that are generated after the irradiation of Au NPs. In this thesis, the proposed approach was to develop radioactive Au NPs as a new brachytherapy treatment for prostate cancer. The unique and innovative aspect of this strategy was to synthesize core-shell NPs based on the radioisotope palladium-103 (103Pd, 20 keV), which is currently used in low-dose rate prostate cancer brachytherapy. In this concept, the administrated dose is increased via the radiosensitization effect that is generated through the interactions of low-energy photons with the gold atoms. The proof-ofconcept of this approach was first demonstrated by the synthesis and characterization of the core-shell NPs (103Pd@Au-PEG NPs). Then, a longitudinal in vivo study following the injection of NPs in a prostate cancer xenograft murine model was performed. The therapeutic efficiency was confirmed by the tumor growth delay of the treated group as compared to the control group (untreated). Finally, a mapping study of the dose distribution generated by the NPs at the cellular and tumor levels provided new insights about the therapeutic mechanisms related to radioactive NPs. In summary, the studies presented in this thesis are precursors works in the field of nanobrachytherapy, and could pave the way for a new generation of NPs for radiotherapy

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging