Niloufar Zarinabad Nooralipour1, Amedeo
Chiribiri1, Gilion Hautvast2, Aruna Vishnu Arujuna1,
Eike Nagel1, Philip Batchelor3
1The Centre of Excellence
in Medical Engineering, Kings College London, London, United Kingdom; 2Imaging
Systems- MR, Philips Healthcare, Netherlands; 3The Centre of
Excellence in Medical Engineering,
Kings College London, London, United Kingdom
In this study four different deconvolution algorithms have been applied to voxel-wise analysis of first-pass myocardial perfusion MR data. We aimed to test robustness of these methods to noise and to evaluate the accuracy of the perfusion estimates by validating the estimated perfusion values in a hardware perfusion phantom with true perfusion values, measured by means of precision flow-meters. This study demonstrated that Auto-Regressive Moving Average model is the least sensitive method to noise, while achieving almost similar accuracy to exponential basis deconvolution, which is the superior model in terms of accuracy of estimation, and compares favourably over the Fermi function modelling quantification method