Quantitative myocardial perfusion magnetic resonance imaging: the impact of pulsatile flow on contrast agent bolus dispersion

Abstract

Myocardial blood flow (MBF) can be quantified using T1-weighted first-pass magnetic resonance imaging (MRI) in combination with a tracer-kinetic model, like MMID4. This procedure requires the knowledge of an arterial input function which is usually estimated from the left ventricle (LV). Dispersion of the contrast agent bolus may occur between the LV and the tissue of interest. The aim of this study was to investigate the dispersion under conditions of physiological pulsatile blood flow, and to simulate its effect on MBF quantification. The dispersion was simulated in coronary arteries using a computational fluid dynamics (CFD) approach. Simulations were accomplished on straight vessels with stenosis of different degrees and shapes. The results show that dispersion is more pronounced under resting conditions than during hyperemia. Stenosis leads to a reduction of dispersion. In consequence, dispersion results in a systematic MBF underestimation between -0.4% and -9.3%. The relative MBF error depends not only on the dispersion but also on the actual MBF itself. Since MBF under rest is more underestimated than under stress, myocardial perfusion reserve is overestimated between 0.1% and 4.5%. Considering other sources of errors in myocardial perfusion MRI, systematic errors of MBF by bolus dispersion are relatively small.

Computational Fluid Dynamics Simulations of Contrast Agent Bolus Dispersion in a Coronary Bifurcation: Impact on MRI-Based Quantification of Myocardial Perfusion

“…This fact is an important issue since the flow rate stays constant inside the arterial tree if the assumption of rigid vessel walls is made. Therefore, an increase in area corresponds to a decrease in blood velocity, whereas the average velocity is an important factor influencing the dispersion of the contrast agent [16]. …”

“…The mass fraction of the injected contrast agent bolus Y CA can be described by a gamma-variate function at the inlet of our geometry [15, 16, 30] $\begin{array}{c}{Y}_{\text{CA}}\left(t,z=\mathrm{0}\right)=\{\begin{array}{cc}{a0.75em0.75em(t-t_{\mathrm{normal0}}0.75em0.75em)}^b{e}^{-c0.75em0.75em(t-t_{\mathrm{normal0}}0.75em0.75em)}\hfill & t>t_{\mathrm{normal0}},\hfill \\ \mathrm{normal0}\hfill & t\le t_{\mathrm{normal0}},\hfill \end{array}\end{array}$ where the parameters have been estimated by fitting the AIF measured inside the LV of a volunteer MRI measurement as a = 1.013 × 10 −3 , b = 2.142, and c = 0.454 s −1 . The parameter t 0 describes the delayed arrival of the bolus.…”

“…Blood was considered to be a Newtonian fluid, and a typical density of ρ = 1050 kg/m 3 and a dynamic viscosity of η = 0.004 kg/(m∗s) were chosen. The diffusion coefficient of the contrast agent in blood at body temperature was estimated by Graafen et al to be D = 1.5∗10 −10  m 2 /s [16]. In this case Gd-DTPA, which is a typical contrast agent used at clinical MRI measurements, was considered.…”

“…Mathematically, dispersion of the AIF can be specified by the convolution of the undispersed AIF LV , which is usually measured in the LV, and a convolution kernel, which is typically called vascular transport function (VTF) [14–16, 33] $\begin{array}{c}{\text{AIF}}_{\text{toi}}=\text{VTF}\otimes {\text{AIF}}_{\text{LV}},\end{array}$ where AIF toi describes the true dispersed AIF at the tissue of interest. The VTF is a function of time and space along the vessel.…”

“…Calamante et al performed CFD simulations to estimate the dispersion of a contrast agent bolus in cerebral vessels [14]. Previous CFD simulations examining the bolus dispersion in coronary vessels performed by Graafen et al used an idealized single vessel geometry considering both constant [15] and pulsatile [16] flows. Both demonstrated an underestimation of the MBF and an overestimation of the MPR if bolus dispersion is neglected.…”