Noninvasive characterization of myocardial tissue damage applying cardiac magnetic resonance

Einführung: Kardiale Magnetresonanztomografie mit der Anwendung von Kontrastmitteln ist eine etablierte klinische Untersuchung zur Differenzierung von Myokardschäden. Die Auswertung der Bilddaten basiert zu einem großen Teil auf subjektiv geführten Methoden. Die Quantifizierung relevanter pathophysiologischer Prozesse könnte zur Objektivierung der Untersuchung beitragen. Daher ist es unser Ziel, ein Verfahren vorzuschlagen, die Pathophysiologie von myokardialen Ödemen und Fibrose zu charakterisieren. Methoden: Mittels Computational Fluid Dynamics (CFD) wurde der Kontrastmittelfluss in pathologischem Gewebe modelliert. Die Modellparameter ExR, welcher den Austausch von Kontrastmittel (KM) zwischen dem vaskulären und extravaskulären Raum darstellt, wurde verwendet, um unterschiedliche Flussszenarien zu erzeugen. Die Simulationen wurden dann mit quantitativen T1-Maps des Herzens von n = 18 Patienten mit akuter und geheilter Myokarditis sowie alters- und geschlechtsangepassten Probanden aus einer zuvor veröffentlichten Studie verglichen. T1-Maps wurden von der medialen Schicht vor und 1,3,5,7 und 10 Minuten nach der KM-Verabreichung verwendet. Ergebnisse: Der Vorgang zum Lesen und Registrieren aller T1-Maps des Myokards und die Umrechnung auf die KM-Konzentration war bei 10 aufeinander abgestimmten Gruppen von akuten und geheilten Patienten sowie gesunden Probanden erfolgreich. Die simulierten KM Auswaschungs-Kurven wurden an die Messungen in der Austauschrate ExR mit einem Fehler von weniger als 5% gefittet. Signifikante Unterschiede (P <0,05) wurden zwischen akuten und geheilt Patienten sowie geheilten Patienten und Probanden gefunden. Ein größerer Unterschied (P <0,01) wurde zwischen akuten Patienten und Probanden festgestellt. Fazit: Unsere Ergebnisse deuten darauf hin, dass CFD für die Simulation von pathologischem und gesundem Myokardgewebe eingesetzt werden kann. Moderne Machine Learning6 Techniken können in Zukunft mit quantitativen Merkmalen wie der Austauschrate ExR oder anderen T1-Map Merkmalen angewendet werden um myokardiale Schäden zu differenzieren.Introduction: Cardiac magnetic resonance imaging with the application of contrast media is a clinical examination for the differentiation of myocardial damage. The evaluation of the images is based to a large extent on subjectively guided methods. The quantification of relevant pathophysiological processes could contribute to the objectification of the investigation. Therefore, our aim is to propose a method to quantify the pathophysiology in myocardial edema and fibrosis. Methods: Using Computational Fluid Dynamics (CFD), the flow of contrast agent in pathological tissue was modeled. The model parameter ExR governing the exchange of contrast medium(CM) between the vascular and extravascular space was used to generate different flow scenarios. The simulations were then compared to quantitative cardiac T1 maps from n=18 patients with acute and healed myocarditis as well as age- and sexmatched volunteers from a previously published study. T1 maps had been acquired of the medial slice before and 1,3,5,7 and 10 minutes after CM administration. Results: The pipeline of reading and registering all myocardial T1 maps and conversion to CM concentration was successful in 10 matched groups of acute and healed patients as well as volunteers. The simulated CM washout curves were fitted to the measurements in the exchange rate ExR with an error of less than 5%. Significant differences (P<0.05) were found between acute and healed patients, as well as healed patients to volunteers. A greater difference (P<0.01) was found between acute patients and volunteers. Conclusion: Our results suggest the feasibility of using CFD for the simulation of pathologic and healthy myocardial tissue. Modern Machine Learning techniques can be applied in the future for the differentiation of myocardial tissue using quantitative features such as the exchange rate ExR or other T1 map characteristics

Simulation of the Perfusion of Contrast Agent Used in Cardiac Magnetic Resonance: A Step Toward Non-invasive Cardiac Perfusion Quantification

This work presents a new mathematical model to describe cardiac perfusion in the myocardium as acquired by cardiac magnetic resonance (CMR) perfusion exams. The combination of first pass (or contrast-enhanced CMR) and late enhancement CMR is a widely used non-invasive exam that can identify abnormal perfused regions of the heart via the use of a contrast agent (CA). The exam provides important information to the diagnosis, management, and prognosis of ischemia and infarct: perfusion on different regions, the status of microvascular structures, the presence of fibrosis, and the relative volume of extracellular space. This information is obtained by inferring the spatiotemporal dynamics of the contrast in the myocardial tissue from the acquired images. The evaluation of these physiological parameters plays an important role in the assessment of myocardial viability. However, the nature of cardiac physiology poses great challenges in the estimation of these parameters. Briefly, these are currently estimated qualitatively via visual inspection of images and comparison of relative brightness between different regions of the heart. Therefore, there is a great urge for techniques that can help to quantify cardiac perfusion. In this work, we propose a new mathematical model based on multidomain flow in porous media. The model is based on a system of partial differential equations. Darcy's law is used to obtain the pressure and velocity distribution. CA dynamics is described by reaction-diffusion-advection equations in the intravascular space and in the interstitial space. The interaction of fibrosis and the CA is also considered. The new model treats the domains as anisotropic media and imposes a closed loop of intravascular flow, which is necessary to reproduce the recirculation of the CA. The model parameters were adjusted to reproduce clinical data. In addition, the model was used to simulate different scenarios: normal perfusion; endocardial ischemia due to stenosis in a coronary artery in the epicardium; and myocardial infarct. Therefore, the computational model was able to correlate anatomical features, stenosis and the presence of fibrosis, with functional ones, cardiac perfusion. Altogether, the results suggest that the model can support the process of non-invasive cardiac perfusion quantification

A spatially-distributed computational model to quantify behaviour of contrast agents in MR perfusion imaging

Contrast agent enhanced magnetic resonance (MR) perfusion imaging provides an early, non-invasive indication of defects in the coronary circulation. However, the large variation of contrast agent properties, physiological state and imaging protocols means that optimisation of image acquisition is difficult to achieve. This situation motivates the development of a computational framework that, in turn, enables the efficient mapping of this parameter space to provide valuable information for optimisation of perfusion imaging in the clinical context. For this purpose a single-compartment porous medium model of capillary blood flow is developed which is coupled with a scalar transport model, to characterise the behaviour of both blood-pool and freely-diffusive contrast agents characterised by their ability to diffuse through the capillary wall into the extra-cellular space. A parameter space study is performed on the nondimensionalised equations using a 2D model for both healthy and diseased myocardium, examining the sensitivity of system behaviour to Peclet number, Damköhler number (Da), diffusivity ratio and fluid porosity. Assuming a linear MR signal response model, sample concentration time series data are calculated, and the sensitivity of clinically-relevant properties of these signals to the model parameters is quantified. Both upslope and peak values display significant non-monotonic behaviour with regard to the Damköhler number, with these properties showing a high degree of sensitivity in the parameter range relevant to contrast agents currently in use. However, the results suggest that signal upslope is the more robust and discerning metric for perfusion quantification, in particular for correlating with perfusion defect size. Finally, the results were examined in the context of nonlinear signal response, flow quantification via Fermi deconvolution and perfusion reserve index, which demonstrated that there is no single best set of contrast agent parameters, instead the contrast agents should be tailored to the specific imaging protocol and post-processing method to be used