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.

Les propriétés microvasculaires de la moëlle osseuse (MO) sont mal connues chez l’être humain. L’IRM de perfusion en permet une évaluation quantitative non invasive. La hanche a été choisie, car elle est la cible de pathologies fréquentes et handicapantes, qu’il est nécessaire de diagnostiquer plus précocement telle la coxarthrose. Nous avons d’abord implémenté une séquence IRM volumique à voxels isotropiques, avec une couverture large et une résolution spatiale élevée. Celle-ci a ensuite permis l’étude d’une série de 60 patients âgés de 18 à 60 ans, sans antécédent de pathologie osseuse et présentant une MO d’aspect normal en IRM. Les paramètres de perfusion semi-quantitatifs et pharmacocinétiques ont été mesurés dans 15 régions d’intérêt chez chaque patient. Tous les paramètres de perfusion diffèrent entre les zones de MO rouge et de MO jaune. La perfusion est différente entre les MO acétabulaire (squelette axial) et fémorale intertrochantérienne (squelette appendiculaire). Plusieurs paramètres sont corrélés de manière négative à l’âge. Plusieurs paramètres sont différents entre les hommes et les femmes. La perfusion de la tête fémorale est hétérogène, probablement en raison de l’exposition aux contraintes mécaniques. Les paramètres Ktrans, Kep et TTP sont corrélés à l’indice de masse corporelle, ce qui suggère que l’obésité influence le métabolisme de la MO. Enfin, le tabagisme et l’hypercholestérolémie ont une incidence sur ces mêmes paramètres dans certaines zones. Ils pourraient donc être le reflet d’altérations de la microvascularisation osseuse. Ces travaux ouvrent de nouvelles perspectives de recherche sur la physiologie et la pathologie de la MO. / Bone Marrow (BM) microvascular properties are insufficently known in humans. Dynamic-Contrast Enhanced (DCE) MRI allows its non-invasive quantitative assessment. We concentrated on the hip because this joint is frequently affected by debilitating pathologies such as osteoarthritis. Their early diagnosis is a current medical challenge. We implemented a 3D DCE-MRI sequence with isotropic voxels, high spatial resolution and a large coverage. It was used in a study of 60 patients aged 18 to 60, with no previous history of bone disease and with normal-appearing BM on MR images. Semi-quantitative and pharmacokinetic parameters were measured in 15 regions of interest in each patient. All the parameters were different between red and yellow BM. Perfusion was different between acetabular (axial skeleton) and femoral intertrochanteric (appendicular skeleton) BMs. Several parameters were negatively correlated with age. Perfusion was different in men and women. The femoral head perfusion was heterogeneous, likely because of mechanical load exposure. Ktrans, Kep and TTP were correlated with body mass index. This suggests that obesity influences BM metabolism. Smoking and hypercholesterolemia influenced these same parameters in several zones. We hypothesized that these parameters might reflect BM microvascular aletrations. Our results open new research perspectives both in the physiology and pathology of BM.

Microvascular permeability is a pharmacologic indicator of tumor response to therapy, and it is expected that this biomarker will evolve into a clinical surrogate endpoint and be integrated into protocols for determining patient response to antiangiogenic or antivascular therapies. The goal of this research is to develop a method by which microvascular permeability (Ktrans) and vascular volume (vp) as measured by DCE-MRI were directly compared to the same parameters measured by intravital fluorescence microscopy in an MRI-compatible window chamber model. Dynamic contrast enhanced-MRI (DCE-MRI) is a non-invasive, clinically useful imaging approach that has been used extensively to measure active changes in tumor microvascular hemodynamics. However, uncertainties exist in DCE-MRI as it does not interrogate the contrast reagent (CR) itself, but the effect of the CR on tissue water relaxivity. Thus, direct comparison of DCE-MRI with a more quantitative measure would help better define the derived parameters. The combined imaging system was able to obtain both dynamic contrast-enhanced MRI data high spatio-termporal resolution fluorescence data following injection of fluorescent and gadolinium co-labeled albumin. This approach allowed for the cross-validation of vascular permeability data, in relation tumor growth, angiogenesis and response to therapy in both imaging systems.

Background: Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-MRI) are associated with stroke risk indices, but studies have only evaluated symptomatic arteries. I hypothesized that DCE-MRI parameters are different between carotid atherosclerotic plaques at high and low risk for precipitating ischemic stroke. Methods: High and low risk carotid plaques undergoing nonmodel-based DCE-MRI (n=18) were compared using two independent schema: 1) clinical standard (high risk defined as ipsilateral stroke/TIA <1 week old or stenosis >70%); 2) MRI standard (high risk defined as presence of intraplaque hemorrhage [IPH]). Results: IPH-positive plaques (n=9) exhibited greater area under the curve, early and late enhancement rate, and peak enhancement than IPH-negative plaques (n=9) (p<0.05 for all). High (n=8) and low (n=7) risk plaques defined by clinical criteria were not differentiated by any DCE-MRI parameters. Conclusions: Nonmodel-based DCE-MRI discriminates high versus low risk carotid plaque based on the presence of IPH, but not by clinical criteria.

Background: Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-MRI) are associated with stroke risk indices, but studies have only evaluated symptomatic arteries. I hypothesized that DCE-MRI parameters are different between carotid atherosclerotic plaques at high and low risk for precipitating ischemic stroke. Methods: High and low risk carotid plaques undergoing nonmodel-based DCE-MRI (n=18) were compared using two independent schema: 1) clinical standard (high risk defined as ipsilateral stroke/TIA <1 week old or stenosis >70%); 2) MRI standard (high risk defined as presence of intraplaque hemorrhage [IPH]). Results: IPH-positive plaques (n=9) exhibited greater area under the curve, early and late enhancement rate, and peak enhancement than IPH-negative plaques (n=9) (p<0.05 for all). High (n=8) and low (n=7) risk plaques defined by clinical criteria were not differentiated by any DCE-MRI parameters. Conclusions: Nonmodel-based DCE-MRI discriminates high versus low risk carotid plaque based on the presence of IPH, but not by clinical criteria.

Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities. Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities. Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities. Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23469号 / 医博第4776号 / 新制||医||1053(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 溝脇 尚志, 教授 武藤 学, 教授 森田 智視 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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