This dissertation deals with quantitative perfusion analysis of MRI contrast-enhanced image time sequences. It focuses on two so far separately used methods -- Dynamic contrast-enhanced MRI (DCE-MRI) and Dynamic susceptibility contrast MRI (DSC-MRI). The common problem of such perfusion analyses is the unreliability of perfusion parameters estimation. This penalizes usage of these unique techniques on a regular basis. The presented methods are intended to improve these drawbacks, especially the problems with quantification in DSC in case of contrast agent extravasation and instability of the deconvolution process in DCE using advanced pharmacokinetic models. There are a few approaches in literature combining DCE and DSC to estimate new parameters of the examined tissue, namely the relaxivity of the vascular and of the interstitial space. Originally, in this scheme, the 2CXM DCE model was used. Here various models for DCE analysis are tested keeping in mind the DCE-DSC combination. The ATH model was found to perform better in this setting compared to 2CXM. Finally, the ATH model was used in alternating DCE-DSC optimization algorithm and then in a truly fully simultaneous DCE-DSC. The processing was tested using simulated and in-vivo data. According to the results, the proposed simultaneous algorithm performs better in comparison with sequential DCE-DSC, unleashing full potential of perfusion analysis using MRI.

The studies presented here evaluate the biliary, parenchymal and vascular enhancement effects of two T1-shortening liver-specific contrast agents, Gd-BOPTA and Gd-EOB-DTPA, in Magnetic Resonance Imaging (MRI) of healthy subjects and of patients. Ten healthy volunteers were examined with both contrast agents in a 1.5 T MRI system using three-dimensional gradient echo sequences for dynamic imaging until five hours after injection. The enhancement of the common hepatic duct in contrast to the liver parenchyma was analyzed in the first study. This was followed by a study of the image contrasts of the hepatic artery, portal vein and middle hepatic vein versus the liver parenchyma. While Gd-EOB-DTPA gave an earlier and more prolonged enhancement and image contrast of the bile duct, Gd-BOPTA achieved higher maximal enhancement and higher image contrast for all vessels studied during the arterial and portal venous phases. There was no significant difference in the maximal enhancement obtained in the liver parenchyma. In a third study, another 10 healthy volunteers were examined with the same protocol in another 1.5 T MRI system. Using signal normalization and a more quantitative, pharmacokinetic analysis, the hepatocyte-specific uptake of Gd-EOB-DTPA and Gd-BOPTA was calculated. A significant between-subjects correlation of the uptake estimates was found and the ratio of these uptake rates was of the same magnitude as has been reported in pre-clinical studies. The procedure also enabled quantitative analysis of vascular enhancement properties of these agents. Gd-BOPTA was found to give higher vessel-to-liver contrast than Gd-EOB-DTPA when recommended doses were given. In the final study, retrospectively gathered datasets from patients with hepatobiliary disease were analyzed using the quantitative estimation of hepatic uptake of Gd-EOB-DTPA described in the third study. The uptake rate estimate provided significant predictive ability in separating normal from disturbed hepatobiliary function, which is promising for future evaluations of regional and global liver disease. In conclusion, the differing dynamic enhancement profiles of the liver-specific contrast agents presented here can be beneficial in one context and challenging in another. Diseases of the liver and biliary system may affect the vasculature, parenchyma or biliary excretion, or a combination of these. The clinical context in terms of the relative importance of vascular, hepatic parenchymal and biliary processes should therefore determine the contrast agent for each patient and examination. A quantitative approach to analysis of contrast-enhanced liver MRI examinations is feasible and may prove valuable for their interpretation.

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.

<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>

Background Mathematical modeling combined with prior knowledge of the pharmacokinetics of the liver specific contrast agent Gd-EOB-DTPA has the potential to extract more information from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data than previously possible. The ultimate goal of that work is to create a liver model that can describe DCE-MRI data well enough to be used as a diagnostic tool in liver function evaluation. Thus far this goal has not been fully reached and there is still some work to be done in this area. In this thesis, an already existing liver model will be implemented in the software Wolfram SystemModeler (WSM), the corresponding modeling framework will be further developed to better handle the temporally irregular sampling of DCE-MRI data and finally an attempt will be made to determine an optimal sampling design in terms of when and how often to collect images. In addition to these original goals, the work done during this project revealed two more issues that needed to be dealt with. Firstly, new standard deviation (SD) estimation methods regarding non-averaged DCE-MRI data were required in order to statistically evaluate the models. Secondly, the original model’s poor capability of describing the early dynamics of the system led to the creation of an additional liver model in attempt to model the bolus effect. Results The model was successfully implemented in WSM whereafter regional optimization was implemented as an attempt to handle clustered data. Tests on the available data did not result in any substantial difference in optimization outcome, but since the analyses were performed on only three patient data sets this is not enough to disregard the method. As a means of determining optimal sampling times, the determinant of the inverse Fisher Information Matrix was minimized, which revealed that frequent sampling is most important during the initial phase (~50-300 s post injection) and at the very end (~1500-1800 s). Three new means of estimating the SD were proposed. Of these three, a spatio-temporal SD was deemed most reasonable under the current circumstances. If a better initial fit is achieved, yet another method of estimating the variance as an optimization parameter might be implemented.    As a result of the new standard deviation the model failed to be statistically accepted during optimizations. The additional model that was created to include the bolus effect, and therefore be better able to fit the initial phase data, was also rejected. Conclusions The value of regional optimization is uncertain at this time and additional tests must be made on a large number of patient data sets in order to determine its value. The Fisher Information Matrix will be of great use in determining when and how often to sample once the model has achieved a more acceptable model fit in both the early and the late phase of the system. Even though the indications that it is important to sample densely in the early phase is rather intuitive due to a poor model fit in that region, the analyses also revealed that the final observations have a relatively high impact on the model prediction error. This was not previously known. Hence, an important measurement of how suitable the sampling design is in terms of the resulting model accuracy has been suggested. The original model was rejected due to its inability to fit the data during the early phase. This poor initial fit could not be improved enough by modelling the bolus effect and so the new implementation of the model was also rejected. Recommendations have been made in this thesis that might assist in the further development the liver model so that it can describe the true physiology and behaviour of the system in all phases. Such recommendations include, but are not limited to, the addition of an extra blood plasma compartment, a more thorough modelling of the spleen’s uptake of the contrast agent and a separation of certain differing signals that are now averaged.

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.

Glioblastoma multiform (GBM) is an aggressive primary brain tumour. Despite treatment advances in recent years, outcomes remain poor. Disease progression tends to occur adjacent to the original tumour or surgical resection bed, usually within the radiotherapy planning field. This local recurrence and progression is believed to be the result of invasive disease in the surrounding tissue at the time of diagnosis and treatment, which is not currently detectable by conventional non-invasive methods. A number of novel therapies are currently under development which target specific aspects of the tumour behaviour, to try and improve outcomes from this devastating disease. Imaging biomarkers are under development, therefore, in order to provide a non-invasive assessment of tumour extent and behaviour, to provide bespoke image-guided therapies, and detect recurrence or treatment failure at the earliest opportunity. These are also of value in the context of novel therapeutics, which may have a very specific affect on an aspect of tumour behaviour that is not readily apparent on standard clinical imaging. Key to the progression of GBM is the invasion into surrounding white matter. This is followed by a period of tumour growth and subsequent angiogenesis in which microvasculature is produce that is distinct from the highly regulated blood-brain barrier. This thesis covers the development of suite of advanced magnetic resonance imaging (MRI) techniques aimed at characterising those very traits of GBM responsible for the aggressiveness and treatment resistance. Repeatability studies are undertaken to determine the performance of the biomarkers in healthy tissues, and also in a range of gliomas. Dynamic Contrast Enhanced (DCE-) and dynamic susceptibility-enhanced (DSC-)MRI are used to provide estimates of perfusion and permeability in the tumour. In order to address the reasons behind preferential invasion of the corpus callosum, they are used in conjunction with ASL to non-invasively map perfusion territories and watershed regions in the brain through perfusion timing parameters. Diffusion Tensor Imaging (DTI) and quantitative magnetisation transfer (qMT) are used to provide complementary information about white matter integrity, in order to identify changes occurring with glioma invasion as early as possible and direct image-guided treatments at presentation. Their complementary nature is assessed by comparing the two parameters simultaneously in white matter. Additionally, one of the qMT parameters which may be related to tissue pH is shown to be sensitive and specific for the detection of high-grade tumour tissue. Finally, a novel multiparametric imaging biomarker is introduced. Tumour surface mapping assesses the boundary between the solid tumour and surrounding tissue in order to identify areas of potential aggressiveness and invasion. Multiple imaging parameters can be combined to improve specificity and sensitivity. Using the diffusion-weighted imaging parameter, mean diffusivity (MD - also referred to as the apparent diffusion coefficient (ADC)), it is shown to be predictive of clinical outcome in a retrospective and prospective study, while a multiparametric example is given indicating the utility as a predicative biomarker for regions of progression and recurrence, and as potential spatial discriminator for image-guided therapies.

The task of this thesis is to study methods for the acquisition perfusní imaging based on dynamic MR imaging with T1 contrast. It describes methods of measurement of T1 relaxation time and the possibility of evaluating the results. It further describes the phantoms and their use. And it is here mentioned for the dynamic acquisition protocol perfusní imaging. There is also described in detail created a program for automatic control of the NMR system. In the experimental measurements are performed on static and dynamic phantom, are also evaluated perfusion parameters from the Flash sequence.