Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

Abstract

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. Methods DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml/(min*g), cardiac output = 3, 5, 8 L/min). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiologic features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 sec sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11,000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. Results After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. Conclusions MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

Evaluation of static and dynamic perfusion cardiac computed tomography for quantitation and classification tasks

“…Calibration of detector noise was validated using a uniform phantom and varying clinical scanner tube current. 15 Simulated myocardial time activity curves had peaks which varied with MBF and occurred within the same time window as clinical CTA acquisitions (∼4 to 8 s after peak opacification of the left ventricle). All of the following results are for radiation dose-matched static and dynamic simulated acquisitions resulting in an approximate effective dose of 4.3 mSv.…”

“…15 A detailed physiological model of cardiac contrast bolus dynamics was treated as ground truth for simulated patients and drives iodine concentrations over time in a modified XCAT digital phantom. 16 A polyenergetic simulator was used to generate the CT projection data from the dynamic phantoms.…”

“…The static image series followed conventional clinical processing and are only beam hardening corrected for water components. Our physiological driving model 15 and CT simulation produced images with myocardial CT numbers at different time points postcontrast injection and at different perfusion levels. Since static cardiac CT acquisition timing is usually based on a timing bolus acquisition monitoring arterial enhancement, we specified the timing of our static acquisition as a delay from the peak opacification of the left ventricular (LV) cavity, which is within 1 to 3 s of peak aortic opacification.…”

Simulation evaluation of quantitative myocardial perfusion assessment from cardiac CT

“…Ground truth iodine kinetic curves were generated with a realistic iodine delivery and exchange model, described in 7 . In brief, this model supported iodine exchange between the capillary and extravascular space.…”

“…Prior work by our group evaluated different MBF models applied to simple time-attenuation-curves, without the inclusion of the confounding effects in realistic CT data including beam-hardening errors and realistic quantum noise 5 . Recently, we presented preliminary results for MBF estimation with realistic CT simulation 6 . In this study, we extend former studies by evaluating a range of realistic CT data acquisitions and add to the quantitative methods additional evaluation of qualitative static perfusion performance as well as comparisons based on grading disease severity.…”