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L’évaluation de la vascularisation tumorale par l’échographie de contraste ultrasonore a montré son intérêt pour déterminer l’efficacité des traitements anti-angiogéniques. Malgré tout, cette technique suscite de nombreux questionnements concernant la sensibilité des méthodes de quantification du signal ultrasonore. Pour répondre à cette problématique, il a été question dans cette thèse de développer la première modélisation numérique de l’écoulement du sang et des agents de contraste dans des réseaux vasculaires pour étudier les méthodes de quantification du signal ultrasonore et leurs sensibilités par rapport à des variations de volume du réseau tumoral et des vitesses du sang. Une première étape de la thèse a consisté à valider, par une comparaison expérimentale, les hypothèses faites pour la modélisation numérique et principalement la prise en compte du sang comme un fluide Newtonien homogène. La modélisation numérique a permis de mettre en évidence les paramètres les plus sensibles aux modifications du débit vasculaire tumorale que sont l’aire sous la courbe, le rehaussement maximal et la pente de la courbe de rehaussement du signal dans le cadre de la méthode semi-quantitative. Lorsqu’il s’agit de suivre les variations du volume vasculaire tumoral, il apparait que la méthode quantitative par deconvolution de la fonction artérielle est plus sensible. Les méthodes de quantification ont également été étudiées par le biais d’une étude in vivo sur 44 souris. Cette approche numérique de l’écoulement des agents de contraste est prometteuse et peut permettre à terme une évaluation plus large des autres méthodes de quantification développées à ce jour pour l’échographie de contraste. / Evaluation of tumor vascularization by dynamic contrast-enhanced ultrasonography showed interest for the assessment of the effectiveness of anti-angiogenic treatments. Nevertheless, this technique raises many questions about the sensitivity of quantification methods of the ultrasound signal. To address this issue, this thesis focused on the development of the first digital modeling of blood flow and contrast agents in vascular networks to study the methods of quantification of the ultrasound signal and theirs sensitivity according to variations of tumor network volume and blood velocity. A first step of the thesis was to validate by an experimental comparison, the assumptions of the digital modeling and mainly the taking into account of the blood as a homogeneous Newtonian fluid. Digital modeling allowed to highlight parameters sensitive to the modification of the blood flow which are in the case of the semi-quantitative method the area under the enhancement curve, the maximum of the enhancement curve and the slope of the enhancement curve. When it comes to follow variations of the tumor vascular volume, it appears that the quantitative method by deconvolution of the arterial function is more sensitive. The quantification methods have also been investigated throught an in vivo study of 44 mice. This digital approach of the flow of the contrast agents is promising and may eventually enable a more extensive evaluation of other quantification methods developed in dynamic contrast-enhanced ultrasonography to date.

Dynamic Contrast Enhanced Computed Tomography is an imaging tool that aids in evaluating functional characteristics in different stages of disease assessment: diagnostic, treatment effectiveness and monitoring. At the present time, following all the technological advances, there remains no universally validated method of quantitative, non-invasive, perfusion imaging. In order to address this challenge, certain quality assurance flow phantoms have been developed. This work presents the first step in the prospective framework of phantom simulations with the goal of enhancing the understanding of contrast agent kinetics. Existing knowledge about a two-compartmental fluid exchange phantom was used to validate the constructed computational fluid dynamics (CFD) simulation model. The sensitivity of various parameters, both in the geometric and computational domains, was determined. Finally, the model was employed to evaluate current perfusion parameter estimation models. This provides the groundwork for future phantom developments within the framework.

Dynamic Contrast Enhanced Computed Tomography is an imaging tool that aids in evaluating functional characteristics in different stages of disease assessment: diagnostic, treatment effectiveness and monitoring. At the present time, following all the technological advances, there remains no universally validated method of quantitative, non-invasive, perfusion imaging. In order to address this challenge, certain quality assurance flow phantoms have been developed. This work presents the first step in the prospective framework of phantom simulations with the goal of enhancing the understanding of contrast agent kinetics. Existing knowledge about a two-compartmental fluid exchange phantom was used to validate the constructed computational fluid dynamics (CFD) simulation model. The sensitivity of various parameters, both in the geometric and computational domains, was determined. Finally, the model was employed to evaluate current perfusion parameter estimation models. This provides the groundwork for future phantom developments within the framework.

L'imagerie par résonance magnétique dynamique (IRM-dynamique) consiste en l'observation de la distribution dans le temps d'un agent de contraste à l'aide de l'IRM. L'une des approches très répandues est d'analyser les données à l'aide de modèles mathématiques qui décrivent la pharmacocinétique de cet agent dans les tissus. L'une des hypothèses utilisées par l'ensemble des modèles présentés dans la littérature à ce jour est que les images d'IRM-dynamique peuvent être analysées pixel-par-pixel ce qui néglige implicitement la possibilité de diffusion de l'agent à l'intérieur des tissus. Dans ce mémoire, un nouveau modèle est proposé dans lequel la diffusion de l'agent est explicitement incluse dans un modèle à deux compartiments. Les deux paramètres couramment utilisés dans la littérature sont : K[indice supérieur trans] , le taux de transfert transcapillaire, et [nu]e, la fraction de volume extravasculaire extracellulaire. Deux méthodes d'optimisation stochastique ont été évaluées pour le lissage avec le modèle proposé à cause de la très grande taille de l'espace des solutions. Le modèle a été caractérisé avec des données simulées incluant la diffusion de l'agent de contraste et des données expérimentales montrant des signes de diffusion à l'intérieur du tissu tumoral. Les résultats avec les données simulées montrent que le modèle peut retrouver de façon fiable les valeurs des paramètres utilisés pour générer ces données (erreur relative moyenne de 16% pour K trans et 17% pour [nu]e) même lorsqu'un niveau de bruit raisonnable est ajouté. Le modèle standard à deux compartiments négligeant la diffusion retourne des distributions de valeurs de K[indice supérieur trans] erronées (erreur relative moyenne de 43%) qui sont moyennée sur le tissu. Lorsque les données expérimentales sont ajustées avec le modèle standard, les valeurs de K[indice supérieur trans] retournées montrent une perfusion moyennée sur l'ensemble du tissu, ce qui n'est pas en accord avec le rehaussement initial du signal qui est observé. À l'opposé, le modèle proposé retourne des cartes de K[indice supérieur trans] ayant une démarcation franche entre les zones bien perfusées et celles très peu perfusées en accord avec ce qui est observé sur les images d'IRM. De plus, le modèle standard à deux compartiments assigne des valeurs n'ayant pas de sens physique à [nu]e ([nu]e [supérieur à] 1) dans le centre des tumeurs où l'agent parvient par diffusion à partir de la périphérie bien vascularisée. De son côté, le modèle proposé trouve des valeurs plausibles de [nu]e pour l'ensemble du tissu.

Angiogenesis is a fundamental driver of tumor biology and many other important aspect of human health. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been shown to be a valuable biomarker for the indirect assessment of angiogenesis. However, DCE-MRI is very specialized technique that has limitations. In this dissertation new models and contrast agents to address some of these limitations are presented. Chapter 1 presents an introduction to DCE-MRI, the rationale to asses tumor biology with this technique, the MRI pulses sequences and the standard pharmacokinetic modeling used for the analysis of DCE- MRI data. Chapter 2 describes the application of DCE-MRI to asses the response to the hypoxia-activated drug TH-302. It is shown that DCE-MRI can detect a response after only 24 hours of initiating therapy. In Chapter 3, a new model for the analysis of DCE-MRI is presented, the so-called Linear Reference Region Model (LRRM). This new model improves upon existing models and it was demonstrated that it is ~620 faster than current algorithms and 5 times less sensitive to noise, and more importantly less sensitive to temporal resolution which enables the analysis of DCE-MRI data obtained in the clinical setting, which opens a new area of study in clinical MRI. Chapter 4 describes the extension of the LRRM to estimate the absolute permeability of two fluorinated contrast agents; we call this approach the Reference Agent Model (RAM). In order to make this new model an experimental reality, a novel pulse sequence and contrast agents (CA) for ¹⁹F MRI were developed. Two contributions to the field of DCE-MRI are presented in this chapter, the first simultaneous ¹⁹F-DCE-MRI detection of two fluorinated CA in a mouse model of breast cancer, and the estimation of their relative permeability. RAM eliminates some of the physiological variables that affect DCE-MRI, which may improve its sensitivity and specificity. Finally, new potential applications of LRRM and RAM are discussed in Chapter 5.

"A one dimensional, multiple reaction pathway model of the dechlorination reactions of trichloroethylene (TCE) and tetrachloroethylene (PCE) as these species pass through a zero valent iron permeable reactive barrier (PRB) was produced. Three different types of rate equations were tested; first order, surface controlled with interspecies competition, and surface controlled with inter and intra species competition. The first order rate equations predicted the most accurate results when compared to actual data from permeable reactive barriers. Sensitivity analysis shows that the most important variable in determining TCE concentration in the barrier is the first order rate constant for the degradation of TCE. The velocity of the water through the barrier is the second most important variable determining TCE concentration. For PCE the concentration in the barrier is most sensitive to the velocity of the water and to the first order degradation rate constant for the PCE to dichloroacetylene reaction. Overall, zero valent iron barriers are more effective for the treatment of TCE than PCE. "

http://pubs.rsna.org/doi/full/10.1148/radiol.2015150416 / 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19575号 / 医博第4082号 / 新制||医||1013(附属図書館) / 32611 / 京都大学大学院医学研究科医学専攻 / (主査)教授 平岡 眞寛, 教授 YOUSSEFIAN Shohab, 教授 増永 慎一郎 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Dynamic contrast enhanced MR imaging (DCE-MRI) has commonly been used to investigate disruptions in microvascular capillary permeability in pathologies such as tumours, and in brain diseases such as multiple sclerosis. This imaging technique involves intravenous injection of a contrast agent, which can modulate MR signal contrast, while frequently acquiring images (i.e. every few seconds) as the agent perfuses through the tissue of interest. Microvascular permeability, and other parameters such as blood volume and flow (perfusion) can be quantified through application of a pharmacokinetic model on the data acquired from the MRI scan. The model requires input from both the biological (e.g. pharmacokinetic rate constants) as well as physical (i.e. scanner settings) parameters. As there are a great many variables and different biophysical models (e.g. high blood flow, high permeability tissues, etc.) there needs to be considerations made for situations where the permeability may be only slightly different from normal. In the brain the blood-brain barrier (BBB) is a highly selective barrier that restricts most bulk diffusion/permeability of solutes. Changes in BBB permeability is likely only subtle in diseases such as depression or bipolar disorder, especially when compared against hypervascular-hypermemeable cancers that are void of a BBB altogether. The problem is however, to decide which model of BBB permeability is best suited for differentiating subtle changes. Thus the intention of this project was to investigate multiple pharmacokinetic models for the tracking of MRI contrast agent in regions of the brain with an intact BBB. In the brain, where there is strict regulation of molecules passing through the microvasculature into the extracellular space, and where more subtle disruptions might be of interest, different assumptions may be necessary. Four models were investigated: the Tofts model, the modified Tofts model, the two-compartment exchange model, and the uptake model. Scans of eight healthy subjects were analyzed, and permeability was quantified using each model. The accuracy of each model, quantified by the R\textsuperscript{2} value, were compared. Analysis found that the Tofts model performed significantly worse than the modified Tofts and Uptake models when fitting regions of the brain with a blood-brain barrier, with a p-value of 0.006. The analysis did not reveal any significant difference between the modified Tofts, Uptake or 2CX models, although perhaps it was obscured due to the limited number of data points. Further investigation is needed to determine any differences between the three top-performing models. / Thesis / Master of Applied Science (MASc)

Tumors have several important hallmarks; anomalous and heterogeneous behaviors of their vascular structures, and high angiogenesis and neovascularization. Tumor tissue presents high blood flow (F) and extraction ratio (E) of contrast molecules. Consequently there is growing interest in non invasive methods for characterizing changes in tumor vasculature. Toft's model has been extensively used in the past in order to calculate Ktrans maps which take into consideration both F and E. However, in this thesis we argue that for accurate tumor characterization we need a model able to compute both F and E in tissue plasma. This project has been developed as part of a larger project, working toward building a Clinical Decision Support System (CDSS): an interactive expert computer software, that helps doctors and other health professionals make decisions regarding patient treatment progress. Using the Gamma Capillary Transit Time (GCTT) pharmacokinetic model we calculate F and E separately in a more realistic framework; unlike other models it takes into account the heterogeneity of the tumor, which depends on parameter a-1. a-1 is the width of the distribution of the capillary transit times within a tissue voxel. In more detail, a-1 expresses the heterogeneity of tissue microcirculation and microvasculature. We studied 9 patients pathologically diagnosed with glioblastoma multiforme (GBM), a common malignant type of brain tumor. Several physiological parameters including the blood flow and extraction ratio distributions were calculated for each patient. Then we investigated if these parameters can characterize early the patients' responsiveness to current treatment; we assessed the classification potential based on the actual therapy outcome. To this end, we present a novel analysis framework which exploits the new parameter a-1 and organizes each voxel into four sub-region. Our results indicate that early characterization of response based on GCCT can be significantly improved by focusing on tumor voxels from a specific sub-region.