Abstract

BackgroundMathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow. Saturation of left ventricle signal during standard contrast administration can compromise the input function used when applying these models. This saturation effect is evident during application of standard Fermi models in single bolus perfusion data. Dual bolus injection protocols have been suggested to eliminate saturation but are much less practical in the clinical setting. The distributed parameter model can also be used for absolute quantification but has not been applied in patients with coronary artery disease. We assessed whether distributed parameter modeling might be less dependent on arterial input function saturation than Fermi modeling in healthy volunteers. We validated the accuracy of each model in detecting reduced myocardial blood flow in stenotic vessels versus gold-standard invasive methods.MethodsEight healthy subjects were scanned using a dual bolus cardiac perfusion protocol at 3T. We performed both single and dual bolus analysis of these data using the distributed parameter and Fermi models. For the dual bolus analysis, a scaled pre-bolus arterial input function was used. In single bolus analysis, the arterial input function was extracted from the main bolus. We also performed analysis using both models of single bolus data obtained from five patients with coronary artery disease and findings were compared against independent invasive coronary angiography and fractional flow reserve. Statistical significance was defined as two-sided P value < 0.05.ResultsFermi models overestimated myocardial blood flow in healthy volunteers due to arterial input function saturation in single bolus analysis compared to dual bolus analysis (P < 0.05). No difference was observed in these volunteers when applying distributed parameter-myocardial blood flow between single and dual bolus analysis. In patients, distributed parameter modeling was able to detect reduced myocardial blood flow at stress (<2.5 mL/min/mL of tissue) in all 12 stenotic vessels compared to only 9 for Fermi modeling.ConclusionsComparison of single bolus versus dual bolus values suggests that distributed parameter modeling is less dependent on arterial input function saturation than Fermi modeling. Distributed parameter modeling showed excellent accuracy in detecting reduced myocardial blood flow in all stenotic vessels.Electronic supplementary materialThe online version of this article (doi:10.1186/s12968-015-0125-1) contains supplementary material, which is available to authorized users.

Highlights

  • Mathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow

  • We demonstrate no difference in myocardial blood flow estimates using the distributed parameter model between single and dual bolus analysis

  • Model comparison in 8 healthy volunteers suggested that single bolus analysis of the distributed parameter model shows no statistically significant difference compared to dual bolus analysis, indicating that this model may be less dependent on arterial input function saturation than the Fermi model

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Summary

Introduction

Mathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow. Myocardial blood flow quantification using modeldependent analysis is based on fitting the convolution of a model with the arterial input function to the tissue contrast agent concentration-time curve. Fermi deconvolution modeling is a popular approach used to estimate myocardial blood flow during the first-pass of gadolinium-based extracellular contrast agents (CA) [3,7,8]. It is an empirical-mathematical model, which is convolved with the first-pass of the arterial input function [3]. In addition to myocardial blood flow, this model can be used to calculate other microvascular characteristics including intravascular space, extravascular-extracellular space, permeability surface area product, extraction fraction and volume of distribution [9]

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