Modelling Parameter Role on Accuracy of Cardiac Perfusion Quantification

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusionCMR postprocessing is to recover tissue impulse-response from observed signalintensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-response. However this variation influences the accuracy of the deconvolution and introduces possible variations in the results. Using an appropriate order for quantification is essential to allow CMR-perfusion-quantification to develop into a useful clinical tool. The aim of this study was to evaluate the effect of parameter variation in Fermi modelling, autoregressive moving-average model (ARMA), B-spline-basis and exponential-basis deconvolution. Whilst Fermi is the least dependent method on the modelling parameter determination, the B-spline and ARMA were the most sensitive models to this variation. ARMA upon a correct choice of order showed to be the superior to other methods.