No description available.

Magnetic resonance spectroscopy has been widely used in medical applications, rendering precise evaluation and diagnosis in clinics. As the development of various tools for automatic spectra analysis, providing objective quantification of metabolites, absolute concentrations has been playing an important role in clinical studies and applications as well. In this study, we investigate the reliability and accuracy of absolute concentration quantified by LCModel. Ten healthy subjects were included. We compared the resultant concentrations calculated by internal water scaling and phantom calibration, both of which are provided by LCModel. Partial volume effect was also taken into account to improve the accuracy of absolute concentrations. Automatic segmentation was applied to volume of interest in order to separate gray matter and white matter, which will facilitate the further partial volume correction and thus better accuracy of absolute quantification.

Magnetic resonance spectroscopy is now in widespread use, which with various tools of spectra analysis can provide concentrations of metabolites. The influence of metabolites on human physiology is greatly. Due to the tiny variation of the concentration in various metabolites, the analytic method used in the quantitative determination of the absolute concentrations of metabolites plays an important role in this research area. In this thesis we present an analysis tool for segmentation of white matter, gray matte and cerebrospinal fluid using region growing with spatial space, and provide manual interaction for exception handling in this subject. Then we use this tool to analyze different percentages of white matter and gray matter with the default parameter by LCModel and correct partial volume effect. The results show that the proposed tool can improve significantly the accuracy in absolute quantitative analysis of concentration.

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.

<p>The present thesis is focused on the development and application of dose response models for radiation therapy. Radiobiological models of tissue response to radiation are an integral part of the radiotherapeutic process and a powerful tool to optimize tumor control and minimize damage to healthy tissues for use in clinical trials. Ideally, the models could work as a historical control arm of a clinical trial eliminating the need to randomize patents to suboptimal therapies. In the thesis overview part, some of the basic properties of the dose response relation are reviewed and the most common radiobiological dose-response models are compared with regard to their ability to describe experimental dose response data for rat spinal cord using the maximum likelihood method. For vascular damage the relative seriality model was clearly superior to the other models, whereas for white matter necrosis all models were quite good except possibly the inverse tumor and critical element models. The radiation sensitivity, seriality and steepness of the dose-response relation of the spinal cord is found to vary considerably along its length. The cervical region is more radiation sensitive, more parallel, expressing much steeper dose-response relation and more volume dependent probability of inducing radiation myelitis than the thoracic part. The higher number of functional subunits (FSUs) consistent with a higher amount of white matter close to the brain may be responsible for these phenomena. With strongly heterogeneous dose delivery and due to the random location of FSUs, the effective size of the FSU and the mean dose deposited in it are of key importance and the radiation sensitivity distribution of the FSU may be an even better descriptor for the response of the organ. An individual optimization of a radiation treatment has the potential to increase the therapeutic window and improve cure for a subgroup of patients.</p>

The recent discovery of the iron-pnictide superconductors has generated tremendous excitement, in part because there are many tantalizing similarities to the cuprate superconductors. As with the cuprates, it is strongly suspected that the spins contribute to superconductivity. There seems to be a strong relationship between the lattice and magnetism in this system. Several authors have discussed the possibility of spin-phonon coupling, but direct experimental evidence has remained elusive. This work discusses the relationship between the spins and the lattice in the $BaFe_{2}As_{2}$ family. We demonstrate the presence of negative thermal expansion in these materials, which is a strong indicator of spin-lattice interaction. In addition, we have conducted inelastic neutron scattering experiments to examine the dynamical relationship between the spins and the lattice. In particular, we make use of the phenomenon known as magnetovibrational scattering to search for evidence of spin-phonon coupling. We believe that this is the first work to use magnetovibrational scattering in an antiferromagnetic system as a tool to study the spin-phonon interaction. Our results provide direct experimental evidence for the existence of spin-phonon coupling, with possible implications about the role of phonons in the superconductivity of iron pnictides.

The present thesis is focused on the development and application of dose response models for radiation therapy. Radiobiological models of tissue response to radiation are an integral part of the radiotherapeutic process and a powerful tool to optimize tumor control and minimize damage to healthy tissues for use in clinical trials. Ideally, the models could work as a historical control arm of a clinical trial eliminating the need to randomize patents to suboptimal therapies. In the thesis overview part, some of the basic properties of the dose response relation are reviewed and the most common radiobiological dose-response models are compared with regard to their ability to describe experimental dose response data for rat spinal cord using the maximum likelihood method. For vascular damage the relative seriality model was clearly superior to the other models, whereas for white matter necrosis all models were quite good except possibly the inverse tumor and critical element models. The radiation sensitivity, seriality and steepness of the dose-response relation of the spinal cord is found to vary considerably along its length. The cervical region is more radiation sensitive, more parallel, expressing much steeper dose-response relation and more volume dependent probability of inducing radiation myelitis than the thoracic part. The higher number of functional subunits (FSUs) consistent with a higher amount of white matter close to the brain may be responsible for these phenomena. With strongly heterogeneous dose delivery and due to the random location of FSUs, the effective size of the FSU and the mean dose deposited in it are of key importance and the radiation sensitivity distribution of the FSU may be an even better descriptor for the response of the organ. An individual optimization of a radiation treatment has the potential to increase the therapeutic window and improve cure for a subgroup of patients.

No description available.

No description available.

Les pathologies cardio-vasculaires d’origine athéroscléreuse, premières causes de mortalité dans les pays occidentaux, sont insuffisamment prises en charge par les outils de dépistage et de suivi thérapeutique actuels. La Tomographie par Emission de Positons (TEP) est susceptible d’apporter au clinicien des outils puissants pour le diagnostic et le suivi thérapeutique des patients, en particulier grâce au traceur Fluorodésoxyglucose marqué au fluor 18 ([18F]-FDG). Cependant, l’Effet de Volume Partiel (EVP), dû notamment à la résolution spatiale limitée dans les images (plusieurs millimètres) en regard des faibles dimensions (de l’ordre du millimètre) des VOlumes d’Intérêt (VOIs), et les fluctuations statistiques du signal mesuré présentent des défis pour une quantification fiable.Un modèle original de lésion athéromateuse, paramétré par ses dimensions et sa concentration d’activité, a été développé et des simulations Monte-Carlo d’acquisitions TEP au [18F]-FDG de 36 lésions ont été produites. A partir des acquisitions simulées, nous avons montré que le nombre d’itérations des reconstructions itératives, le post-filtrage appliqué et le moyennage dans le VOI,paramètres relevés comme hautement variables dans une revue de la littérature dédiée, peuvent induire des variations des valeurs de fixation mesurées d’un facteur 1.5 à 4. Nous avons montré qu’une modélisation de la réponse du tomographe pouvait réduire le biais de mesure d’environ 10% par rapport au biais mesuré sur une image reconstruite avec un algorithme itératif standard et pour un niveau de bruit comparable. Sur les images reconstruites, nous avons montré que la fixation mesurée reste très biaisée (sous-estimation de plus de 50% du SUV réel) et dépend fortement des dimensions de la lésion à cause de l’EVP. Un contraste minimum de 4 par rapport à l’activité sanguine est nécessaire pour qu’une lésion soit détectée. Sans correction d’EVP, la mesure présente une corrélation faible avec la concentration d’activité, mais est très corrélée à l’activité totale dans la lésion. L’application d’une correction d’EVP fournit une mesure moins sensible à la géométrie de la lésion et plus corrélée à la concentration d’activité mais réduit la corrélation à l’activité totale dans la lésion.En conclusion, nous avons montré que l’intégralité de la fixation du [18F]-FDG dans les lésions athéromateuses inflammatoires totale peut être caractérisée sur les images TEP. Cette estimée ne requiert pas de correction de l’EVP. Lorsque la concentration d’activité dans la lésion est estimée, les mesures sont très biaisées à cause de l’EVP. Ce biais peut être réduit en mesurant le voxel d’intensité maximale, dans les images reconstruites sans post-filtrage avec au moins 80 itérations incluant un modèle de réponse du détecteur. La mise en œuvre d’une correction d’EVP facilite la détection des changements d’activité métabolique indépendamment de changements de dimensions de la zone siègede l’inflammation. Une quantification absolue exacte de la concentration d’activité dans les lésions ne sera possible que via une amélioration substantielle de la résolution spatiale des détecteurs TEP. / Cardiovascular disease is the leading cause of death in western countries. New strategies and tools for diagnosis and therapeutic monitoring need to be developed to manage patients with atherosclerosis, which is one major cause of cardiovascular disease. Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a powerful imaging technique that can detect at early stages plaques prone to rupture. Yet, Partial Volume Effect (PVE), due to the small lesion dimensions (around 1 mm) with respect to the scanner spatial resolution (around 6 mm full width at half maximum), and statistical variations considerably challenge the precise characterization of plaques from PET images. An original model of atheromatous lesion parameterized by its dimensions and activity concentration, was developed. Thirty six Monte Carlo simulations of FDG-PET acquisitions were produced. Based on the simulations, we showed that the number of iterations in iterative reconstructions, the post filtering of reconstructed images and the quantification method in the Volume Of Interests (VOI) varied sharply in an analysis of the dedicated literature. Changes in one of these parameters only could induce variations by a factor of 1.5 to 4 in the quantitative index. Overall, inflammation remained largely underestimated (> 50% of the real uptake). We demonstrated that modeling the detector response could reduce the bias by 10% of its value in comparison to a standard OSEM recontruction and for an identical level of noise. In reconstructed images, we showed that the measured values depended not only on the real uptake but also on the lesion dimensions because of PVE. A minimum contrast of 4 with respect to blood activity was required for the lesion to be observable. Without PVE correction, the measured values exhibited a correlation with activity concentration but were much more correlated with the total uptake in the lesion. Applying a PVE correction leads to an activity estimate that was less sensitive to the geometry of the lesion. The corrected values were more correlated to the activity concentration and less correlated to the total activity. In conclusion, we showed that the total activity in inflammatory lesions could be assessed in FDG-PET images. This estimate did not require PVE correction. Tracer concentration estimates are largely biased due to PVE, and the bias can be reduced by measuring the maximum voxel in the lesion in images reconstructed with at least 80 iterations and by modeling the detector response. Explicit PVE correction is recommended to detect metabolic changes independent of geometric changes. An accurate estimation of plaque uptake will however require the intrinsic spatial resolution of PET scanners to be improved.