This thesis deals with perfusion analysis using DCE-MRI (Dynamic contrast-enhanced magnetic resonance imaging). DCE-MRI is commonly used for microcirculation evaluation mainly in oncology and in recent years also in cardiology. The theoretical overview focuses on the issue of pharmacokinetic modeling and the estimation of perfusion parameters using selected models. The experimental part describes research software PerfLab and then it is aimed at the proposed program for synthetic data generation. Simulated data obtained under ideal conditions and in the presence of noise were used to compare models for the accuracy and reliability of DCE-MRI analysis.

Perfusion magnetic resonance imaging is modern diagnostic method used mainly in oncology. In this method, contrast agent is injected to the subject and then is continuously monitored the progress of its concentration in the affected area in time. Correct determination of the arterial input function (AIF) is very important for perfusion analysis. One possibility is to model AIF by multichannel blind deconvolution but the estimated AIF is necessary to be scaled. This master´s thesis is focused on description of scaling methods and their influence on perfussion parameters in dependence on used model of AIF in different tissues.

In translational medicine, complementary functional and morphological imaging techniques are used extensively to observe physiological processes in vivo and to assess structural changes as a result of disease progression. The combination of magnetic resonance imaging (MRI) and positron emission tomography (PET) provides excellent soft tissue contrast from MRI with exceptional sensitivity and specificity from PET. This thesis explores the use of sequentially acquired PET and MR images to improve the quantification of small animal PET data. The primary focus was to improve image-based estimates of the arterial input function (AIF), which defines the amount of PET tracer within blood plasma over time. The AIF is required to produce physiological parameters quantifying key processes such as metabolism or perfusion from dynamic PET images. The gold standard for AIF measurement, however, requires serial blood sampling over the course of a PET scan, which is invasive in rat studies but prohibitive in mice due to small total blood volumes. To address this issue, the geometric transfer matrix (GTM) and recovery coefficient (RC) techniques were applied using anatomical MR images to enable the extraction of partial volume corrected image based AIFs from mouse PET images. A non-invasive AIF extraction method was also developed for rats, beginning with the optimization of an automated voxel selection algorithm to assist in extracting MR contrast agent signal time courses from dynamic susceptibility contrast (DSC) MRI data. This procedure was then combined with dynamic contrast enhanced (DCE) MRI to track a combined injection of Gadolinium-based contrast agent and PET tracer through the rat brain. By comparison with gold standard tracer blood sample data, it was found that normalized MRI-based AIFs could be successfully converted into PET tracer AIFs in the first pass phase when fitted with gamma variate functions. Finally, a MR image segmentation method used to provide PET attenuation correction in mice was validated using the Cambridge split magnet PET/MR scanner?s transmission scanning capabilities. This work recommends that contributions from MR hardware in the PET field of view must be accounted forto gain accurate estimates of tracer uptake and standard uptake values (SUVs). This thesis concludes that small animal MR data taken in the same imaging session can provide non-invasive methods to improve PET image quantification, giving added value to combined PET/MR studies over those conducted using PET alone.

Perfusion magnetic resonance imaging is modern diagnostic method used mainly in oncology. In this method, contrast agent is injected to the subject and then is continuously monitored the progress of its concentration in the affected area in time. Correct determination of the arterial input function (AIF) is very important for perfusion analysis. One possibility is to model AIF by multichannel blind deconvolution but the estimated AIF is necessary to be scaled. This master´s thesis is focused on description of scaling methods and their influence on perfussion parameters in dependence on used model of AIF in different tissues.

This master thesis deals with the contrast agents and their application in the ultrasound perfusion analysis. It is focused on Bolus & Burst method which, as a combination of two approaches that have been used so far, allows an absolute quantification of perfusion parameters in the region of interest. Contrast agent concentration time sequence is modeled as a convolution of the parametrically defined arterial input function and the tissue residual funkction. Thesis discusses different mathematical models of these functions as well as the methods of the parameters estimation. The methods functionality is validated on simulated and also preclinical data.

La modélisation pharmacocinétique en tomographie par émission par positrons (TEP) permet d’estimer les paramètres physiologiques liés à l’accumulation dynamique d’un radiotraceur. Les paramètres estimés sont biaisés par le bruit dans les images TEP dynamiques durant l’ajustement des courbes d’activité des tissus, plus communément appelées TAC de l’anglais Time Activity Curve. La qualité des images TEP dynamiques est limitée par la statistique de comptage et influencée par les paramètres de reconstruction choisis en termes de résolution spatiale et temporelle. Il n’existe pas de recommandations claires pour les paramètres de reconstruction à utiliser pour les images dynamiques TEP. L’objectif de ce projet de maitrise est d’évaluer le biais dans l’estimation des paramètres pharmacocinétiques afin de trouver les paramètres de reconstruction TEP les plus optimaux en termes de résolution spatiale et de niveau de bruit. Plus précisément, ce projet cherche à déterminer quel modèle d’AIF offre les meilleurs ajustements, mais aussi quel modèle de poids permet la meilleure estimation des paramètres pharmacocinétiques pour le modèle à deux compartiments. Ce faisant, il serait possible de mieux planifier la reconstruction d’images TEP dynamique et potentiellement améliorer leur résolution spatiale. Afin de tester les biais dans les paramètres pharmacocinétiques sous différents niveaux de bruit, un objet de référence numérique (DRO) avec les informations trouvées dans la littérature sera construit. Ensuite, des simulations numériques seront effectuées avec ce DRO afin de trouver les paramètres de reconstruction et le niveau de bruit le plus optimal. Un biais réduit des paramètres pharmacocinétiques et une meilleure résolution spatiale des images TEP dynamique permettrait de détecter des cancers ou tumeurs à des stades moins avancés de la maladie, permettant potentiellement un traitement plus efficace et avec moins de séquelles et d’effets secondaires pour les patients. En outre, cela permettrait aussi de visualiser l’hétérogénéité des tumeurs. / Pharmacokinetic models in positron emission tomography (PET) allow for the estimation of physiological parameters linked to the dynamic accumulation of a radiotracer. Estimated parameters are biased by noise in dynamic PET images during the fitting of Time Activity Curves (TAC). Image quality in dynamic PET is limited by counting statistics and influenced by the chosen reconstruction parameters in terms of spatial and temporal resolution. Clear recommendations and guidelines for the reconstruction parameters that should be used do not exist at the moment for dynamic PET. The goal of this masters project is to evaluate the bias in the pharmacokinetic parameters estimation to find the optimal PET reconstruction parameters in terms of spatial resolution and noise levels. More precisely, this project aims to determine which AIF model produces the best fits, but also which weight noise model allows for the best parameters estimation with the two compartment model. It would then be possible to plan the PET image reconstruction more finely and potentially improve spatial resolution. To test the pharmacokinetic parameters’ biases under different noise levels, a Digital Reference Object (DRO) with information and specifications found from the litterature will be built. Then, numerical simulations will be done with that DRO to find the optimal noise level and value for the pharmacokinetic parameter. A reduced bias in these parameters and an improved spatial resolution would allow the detection of tumors or lesions at earlier stages, which could potentially allow for a more potent treatment with less short and long term side effects. It would also allow the visualization and quantification of lesion heterogeneity.