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.

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.

Dynamic contrast-enhanced (DCE) time series analysis techniques are hard to fully validate quantitatively as ground truth microvascular parameters are difficult to obtain from patient data. This thesis presents a software application for generating synthetic image data from known ground truth tracer kinetic model parameters. As an object oriented design has been employed to maximise flexibility and extensibility, the application can be extended to include different vascular input functions, tracer kinetic models and imaging modalities. Data sets can be generated for different anatomical and motion descriptions as well as different ground truth parameters. The application has been used to generate a synthetic DCE-MRI time series of a liver tumour with non-linear motion of the abdominal organs due to breathing. The utility of the synthetic data has been demonstrated in several applications: in the development of an Akaike model selection technique for assessing the spatially varying characteristics of liver tumours; the robustness of model fitting and model selection to noise, partial volume effects and breathing motion in liver tumours; and the benefit of using model-driven registration to compensate for breathing motion. When applied to synthetic data with appropriate noise levels, the Akaike model selection technique can distinguish between the single-input extended Kety model for tumour and the dual-input Materne model for liver, and is robust to motion. A significant difference between median Akaike probability value in tumour and liver regions is also seen in 5/6 acquired data sets, with the extended Kety model selected for tumour. Knowledge of the ground truth distribution for the synthetic data was used to demonstrate that, whilst median Ktrans does not change significantly due to breathing motion, model-driven registration restored the structure of the Ktrans histogram and so could be beneficial to tumour heterogeneity assessments.

In Dynamic Contrast Enhanced MRI there are several steps from the initial signal to obtaining the pharmacokinetic parameters for tumor characterization. The aim of this work was to validate the steps in the flow of data focusing on T1-mapping, Contrast Agent (CA)-quantification and the pharmacokinetical (PK) model, using a digital phantom of a head. In the Digital Phantom tissues are assigned necessary values to obtain both a regular and contrast enhanced (using Parker AIF) representation and simulating an SPGR signal. The data analysis was performed in a software called MICE, as well as the Digital Phantom developed at the department of Radiation Sciences at Umeå University. The method of variable flip angles for the T1-mapping was analyzed with respect to SNR and number of flip angles, finding that the median value in each tissue is correct and stable. A "two point" inversion recovery sequence was tested with optimal combination of inversion times for white matter and CSF and obtaining correct T1-values when the inversion times were close to the tissue T1, otherwise with large deviations seen. Three different methods for CA-quantification were analyzed and a large underestimation was found assuming a linearity between signal and CA-concentration mainly for vessels at about 60%, but also for other tissue such as white matter at about 15%, improving when the assumption was removed. Still there was a noticeable underestimation of 30% and 10% and the quantification was improved further, achieving a near perfect agreement with the reference concentration, taking the T2*-effect into account. Applying Kety-model, discarding the vp-term, Ktrans was found to be stable with respect to noise in the tumor rim but ve noticeably underestimated with about 50%. The effect of different bolus arrival time, shifting the AIF required in the PK-model with respect to the CA-concentration, was tested with values up to 5 s, obtaining up to about 5% difference in Ktrans as well as the effect of a vascular transport function obtained by the means of an effective mean transit time up to 5 s and up to about 5% difference in Ktrans.

Effective early detection and monitoring of patient response to cancer therapy is important for improved patient outcomes, avoiding unnecessary procedures and their associated toxicities, as well as the development of new therapies. Dynamic contrast-enhanced magnetic resonance imaging shows promise as a way to evaluate tumour vasculature and assess the efficacy of new anti-angiogenic drugs. However, unavoidable patient motion can decrease the accuracy of subsequent analyses rendering the data unusable. Motion correction algorithms are challenging to develop for contrast-enhanced data since intensity changes due to contrast-enhancement and patient motion must somehow be differentiated from one another. A novel method is presented that employs a motion-informed intensity correction in order to facilitate the registration of contrast enhanced data. The intensity correction simulates the presence or absence of contrast agent in the image volumes to be registered in an attempt to emulate the level of contrast-enhancement present in a single reference image volume.

Effective early detection and monitoring of patient response to cancer therapy is important for improved patient outcomes, avoiding unnecessary procedures and their associated toxicities, as well as the development of new therapies. Dynamic contrast-enhanced magnetic resonance imaging shows promise as a way to evaluate tumour vasculature and assess the efficacy of new anti-angiogenic drugs. However, unavoidable patient motion can decrease the accuracy of subsequent analyses rendering the data unusable. Motion correction algorithms are challenging to develop for contrast-enhanced data since intensity changes due to contrast-enhancement and patient motion must somehow be differentiated from one another. A novel method is presented that employs a motion-informed intensity correction in order to facilitate the registration of contrast enhanced data. The intensity correction simulates the presence or absence of contrast agent in the image volumes to be registered in an attempt to emulate the level of contrast-enhancement present in a single reference image volume.

This project focused on the pulmonary application of magnetic resonance (MR) quantitative equilibrium signal (qS0) mapping, dynamic oxygen-enhanced (OE-) magnetic resonance imaging (MRI) and dynamic contrast-enhanced (DCE-) MRI in asthma and chronic obstructive pulmonary disease (COPD). Initially, a retrospective analysis of MRI and X-ray computed tomography (CT) data from 24 COPD patients and 12 healthy controls demonstrated that MR qS0 mapping had good one-week reproducibility and was comparable to CT in the localization and quantification of emphysema in patients with COPD. In the same data, a reduced oxygen (O2) delivery signal was detected by dynamic OE-MRI in COPD patients regardless of the presence or absence of emphysema on CT, while a significantly reduced baseline spin-lattice relaxation time (T1air) was only observed in emphysematous COPD. Emphysematous COPD also showed significant correlations between dynamic OE-MRI readouts, i.e. enhancing fraction (EF) and the change in the partial pressure of O2 in lung parenchyma (ΔPO2max), and pulmonary diffusion capacity and CT estimates of emphysema. A prospective pilot study was conducted in 10 asthmatic patients which demonstrated that dynamic OE-MRI readouts, including EF, ΔPO2max and O2 wash-in time constant (τup), were reproducible within one month, sensitive to asthma severity and strongly correlated with spirometric readouts of airway function and lung volume. This was followed by a second prospective intervention study in 30 asthmatic patients and 10 healthy controls which revealed a pattern of decreased O2 delivery signal as a response to salbutamol inhalation in severe asthmatics but not in mild asthmatics or healthy controls using short-term repeated dynamic OE-MRI. In addition, DCE-MRI was also performed on 30 asthmatic patients and 10 healthy subjects. A semi-quantitative analysis demonstrated that contrast agent kinetics in asthmatic lungs were characterised by a reduced first-pass peak (SI%max) and a shallower downslope during the late redistribution phase (kwashout) than was observed in healthy controls, and that these were related to pulmonary function test measurements. An extended Tofts model-based quantitative analysis further revealed a significantly increased fractional extravascular extracellular space (ve) in patients with asthma than in healthy controls while the contrast agent transfer coefficient (Ktrans), an index related to vascular permeability, and the fractional blood plasma volume (vp), did not distinguish asthmatics from controls. In conclusion, this project demonstrated the promise of 1) MR qS0 mapping for the assessment of emphysema in COPD lungs, 2) dynamic OE-MRI for the assessment of impaired pulmonary oxygenation in COPD and asthma and for the monitoring of short-term treatment effects in asthma and 3) DCE-MRI for the evaluation of pulmonary microvascular inflammation in asthma. The non-invasive non-ionizing properties and simple setup requirements make these three proton MRI techniques attractive options in the assessment of structural and functional alterations of the lungs in asthma and COPD in clinical settings.

<p>Abstract</p><p>The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information. </p><p>This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.</p><p>I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising. </p><p>Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI. </p><p>II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response. </p><p> The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment. </p><p>In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.</p> / Dissertation