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Magnetic resonance imaging (MRI) of the liver is an important non-invasive tool for diagnosing liver disease. A key application is dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). With the use of the hepatocyte specific contrast agent (CA) Gd-EOB-DTPA it is now possible to evaluate the liver function. Beyond traditional qualitative evaluation of the DCE-MRI images, parametric quantitative techniques are on the rise which yields more objective evaluations. Systems biology is a gradually expanding field using mathematical modeling to gain deeper mechanistic understanding in complex biological systems. The aim of this thesis to combine these two fields in order to derive a physiologically accurate minimal whole body model that can be used to quantitatively evaluate liver function using clinical DCE-MRI examinations.  The work is based on two previously published sources of data using Gd-EOB-DTPA in healthy humans; i) a region of interest analysis of the liver using DCE-MRI ii) a pre-clinical evaluation of the contrast agent using blood sampling.  The modeling framework consists of a system of ordinary differential equations for the contrast agent dynamics and non-linear models for conversion of contrast agent concentrations to relaxivity values in the DCE-MRI image volumes. Using a χ2-test I have shown that the model, with high probability, can fit the experimental data for doses up to twenty times the clinically used one, using the same parameters for all doses. The results also show that some of the parameters governing the hepatocyte flux of CA can be numerically identifiable. Future applications with the model might be as a basis for regional liver function assessment. This can lead to disease diagnosis and progression evaluation for physicians as well as support for surgeons planning liver resection.

Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.

Le cancer de la prostate est le cancer le plus fréquent chez les hommes. Son développement entraine une néo-angiogénèse qui modifie le réseau capillaire. Il est reconnu que l'IRM dynamique (DCE-MRI) est capable de distinguer ces modifications de la microcirculation physiologique. Cependant, ces images restent difficiles à analyser et à interpréter en routine clinique. Dans cette thèse, nous nous sommes intéressés à la mise en place de méthodes robustes pour l'analyse de ces images. Dans un premier temps, nous traitons les méthodes de quantifications des paramètres pharmacocinétiques. Ainsi, une plateforme logicielle a été construite autour du modèle multi-étapes de Tofts. La validation technique a été conduite en utilisant des images simulées avec connaissance de la vérité de terrain de la distribution des lésions. La validation clinique est en cours dans le service de Radiologie de l'Hôpital Claude Huriez du CHRU de Lille. Parallèlement, nous avons exploré l'application des techniques de traitement des données pour l'analyse non paramétrique et non supervisée des courbes temps-intensités. Nous avons développé une approche originale basée sur la classification spectrale. Cette méthode, basée sur la théorie des graphes, permet le regroupement des signaux après transformation de l'espace de représentation. Par la suite, ces groupes de données peuvent être étiquetés par comparaison avec un signal artériel qui sert de référence. Les expérimentations préliminaires conduites sur les données simulées ainsi que sur des données cliniques montre la faisabilité de l'approche. Les deux approches développées sont complémentaires, l'une donnant des paramètres quantitatifs et l'autre permettant de segmenter les zones cancéreuses. / Prostate cancer is the most common cancer among men. Its developments leads to a neo-angiogenesis that changes the capillary network. It is recognized that the DCE-MRI is able to distinguish these physiological changes in microcirculation. However, the images are difficult to analyze and interpret. In this thesis, we were interested by the development of robust methods for the analysis of these images. Initially, we were focused on pharmacokinetic parameters quantification methods. A software platform was constructed to implement the multi-step Tofts model. Technical validation was performed using simulated images with knowledge of the ground truth. Clinical validation is in progress in the Radiology department of Lille University Hospital. In parallel, we have explored the application of nonparametric and unsupervised techniques of data processing for time-intensity curve analysis. We have developed an original approach based on spectral classification. This method, based on graph theory, allows the grouping of signals after transformation of the space of representation. Subsequently, these groups of data can be labeled by comparison to the arterial signal serving as reference. Preliminary experiments conducted on simulated data as well as clinical data show the feasibility of the approach. The two approaches are complementary, one giving quantitative parameters and the other segmenting the cancerous areas.

<p> </p><p>Acquired data from dynamic contrast enhanced MRI measurements can be used to non-invasively assess tumour vascular characteristics through pharmacokinetic modelling. The modelling requires an arterial input function which is the concentration of contrast agent in the blood reaching the volume of interest as a function of time. The aim of this work is testing and optimizing a turboFLASH sequence to appraise its suitability for measuring the arterial input function by measuring phase.</p><p>Contrast concentration measurements in a phantom were done with both phase and relaxivity techniques. The results were compared to simulations of the experiment conditions to compare the conformance. The results using the phase technique were promising, and the method was carried on to in-vivo testing. The in-vivo data displayed a large signal loss which motivated a new phantom experiment to examine the cause of this signal reduction. Dynamic measurements were made in a phantom with pulsatile flow to mimic a blood vessel with a somewhat modified turboFLASH sequence. The conclusions drawn from analyzing the data were used to further improve the sequence and this modified turboFLASH sequence was tested in an in-vivo experiment. The obtained concentration curve showed significant improvement and was deemed to be a good representation of the true blood concentration.</p><p>The conclusion is that phase measurements can be recommended over relaxivity based measurements. This recommendation holds for using a slice selective saturation recovery turboFLASH sequence and measuring the arterial input function in the neck. Other areas of application need more thorough testing.</p><p> </p>

MRI is a versatile tool for systematically assessing anatomical and functional changes in small animal models of human disease. Its noninvasive nature makes MRI an ideal candidate for longitudinal evaluation of disease progression in mice; however achieving the desired level of statistical power can be expensive in terms of imaging time. This is particularly true for cancer studies, where dynamic contrast-enhanced (DCE-) MRI, which involves the repeated acquisition of anatomical images before, during, and after the injection of a paramagnetic contrast agent, is used to monitor changes in tumor vasculature. A means of reducing the overall time required to scan multiple cohorts of animals in distinct experimental groups is therefore highly desirable. Multiple-mouse MRI, in which several animals are simultaneously scanned in a common MRI system, has been successfully used to improve study throughput. However, to best utilize the next generation of small-animal MRI systems that will be equipped with an increased number of receive channels, a paradigm shift from simultaneously scanning as many animals as possible to scanning a more manageable number, at a faster rate, must be considered. Given a small-animal MRI system with 16 available receive channels, the simulations described in this work explore the tradeoffs between the number of animals scanned at once and the number of array elements dedicated to each animal for maximizing throughput. An array system consisting of 15 receive and 5 transmit coils allows throughput-optimized acceleration of a DCE-MRI protocol by a combination of multi-animal and parallel imaging techniques. The array system was designed and fabricated for use on a 7.0-T / 30-cm MRI system, and tested for high-throughput imaging performance in phantoms. Results indicate that up to a nine-fold throughput improvement is possible without sacrificing image quality compared to standard single-animal imaging hardware. A DCE-MRI study throughput improvement of just over six times that achieved with conventional single-mouse imaging was realized. This system will lower the barriers for DCE-MRI in preclinical research and enable more thorough sampling of disease pathologies that progress rapidly over time. / text

Acquired data from dynamic contrast enhanced MRI measurements can be used to non-invasively assess tumour vascular characteristics through pharmacokinetic modelling. The modelling requires an arterial input function which is the concentration of contrast agent in the blood reaching the volume of interest as a function of time. The aim of this work is testing and optimizing a turboFLASH sequence to appraise its suitability for measuring the arterial input function by measuring phase. Contrast concentration measurements in a phantom were done with both phase and relaxivity techniques. The results were compared to simulations of the experiment conditions to compare the conformance. The results using the phase technique were promising, and the method was carried on to in-vivo testing. The in-vivo data displayed a large signal loss which motivated a new phantom experiment to examine the cause of this signal reduction. Dynamic measurements were made in a phantom with pulsatile flow to mimic a blood vessel with a somewhat modified turboFLASH sequence. The conclusions drawn from analyzing the data were used to further improve the sequence and this modified turboFLASH sequence was tested in an in-vivo experiment. The obtained concentration curve showed significant improvement and was deemed to be a good representation of the true blood concentration. The conclusion is that phase measurements can be recommended over relaxivity based measurements. This recommendation holds for using a slice selective saturation recovery turboFLASH sequence and measuring the arterial input function in the neck. Other areas of application need more thorough testing.

This thesis presents a novel set of image analysis tools developed for the purpose of assisting radiologists with the task of detecting and characterizing breast lesions in image data acquired using magnetic resonance imaging (MRI). MRI is increasingly being used in the clinical setting as an adjunct to x-ray mammography (which is, itself, the basis of breast cancer screening programs worldwide) and ultrasound. Of these imaging modalities, MRI has the highest sensitivity to invasive cancer and to multifocal disease. MRI is the most reliable method for assessing tumour size and extent compared to the gold standard histopathology. It also shows great promise for the improved screening of younger women (with denser, more radio opaque breasts) and, potentially, for women at high risk. Breast MRI presently has two major shortcomings. First, although its sensitivity is high its specificity is relatively poor; i.e. the method detects many false positives. Second, the method involves acquiring several high-resolution image volumes before, during and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These shortcomings have motivated the research and development of the computer-aided detection systems designed to improve the efficiency and accuracy of interpretation by the radiologist. Whilst such systems have helped to improve the sensitivity/specificity of interpretation, it is the premise of this thesis that further gains are possible through automated image analysis. However, the automated analysis of breast MRI presents several technical challenges. This thesis investigates several of these, noise filtering, parametric modelling of contrast enhancement, segmentation of suspicious tissue and quantitative characterisation and classification of suspicious lesions. In relation to noise filtering, a new denoising algorithm for dynamic contrast-enhanced (DCE-MRI) data is presented, called the Dynamic Non-Local Means (DNLM). The DCE-MR image data is inherently contaminated by Rician noise and, additionally, the limited acquisition time per volume and the use of fat-suppression diminishes the signal-to-noise ratio. The DNLM algorithm, specifically designed for the DCE-MRI, is able to attenuate this noise by exploiting the redundancy of the information between the different temporal volumes, while taking into account the contrast enhancement of the tissue. Empirical results show that the algorithm more effectively attenuates noise in the DCE-MRI data than any of the previously proposed algorithms. In relation to parametric modelling of contrast enhancement, a new empiric model of contrast enhancement has been developed that is parsimonious in form. The proposed model serves as the basis for the segmentation and feature extraction algorithms presented in the thesis. In contrast to pharmacokinetic models, the proposed model does not rely on measured parameters or constants relating to the type or density of the tissue. It also does not assume a particular relationship between the observed changes in signal intensity and the concentration of the contrast agent. Empirical results demonstrate that the proposed model fits real data better than either the Tofts or Brix models and equally as well as the more complicated Hayton model. In relation to the automatic segmentation of suspicious lesions, a novel method is presented, based on seeded region growing and merging, using criteria based on both the original image MR values and the fitted parameters of the proposed model of contrast enhancement. Empirical results demonstrate the efficacy of the method, both as a tool to assist the clinician with the task of locating suspicious tissue and for extracting quantitative features. Finally, in relation to the quantitative characterisation and classification of suspicious lesions, a novel classifier (i.e. a set of features together with a classification method) is presented. Features were extracted from noise-filtered and segmented-image volumes and were based both on well-known features and several new ones (principally, on the proposed model of contrast enhancement). Empirical results, based on routine clinical breast MRI data, show that the resulting classifier performs better than other such classifiers reported in the literature. Therefore, this thesis demonstrates that improvements in both sensitivity and specificity are possible through automated image analysis.