Abstract
Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusion-CMR post- processing is to recover tissue impulse-response from observed signal-intensity 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.
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Zarinabad, N. et al. (2013). Modelling Parameter Role on Accuracy of Cardiac Perfusion Quantification. In: Ourselin, S., Rueckert, D., Smith, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2013. Lecture Notes in Computer Science, vol 7945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38899-6_44
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