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Population AIF for lung perfusion
Population Arterial Input Function for Lung Perfusion Imaging
Marta Tibiletti, Jo Naish, John C Waterton, Paul JC Hughes, James A Eaden, James M Wild, Geoff JM Parker
ISMRM Conference 2021
Introduction: T1-weighted contrast agent (CA)-based perfusion imaging can be used to characterize the first pass of a CA bolus through the lung, allowing for the measurement of blood flow, relative blood volume and mean transit time.
One of the method’s challenges is the accurate extraction of the Arterial Input Function (AIF), the concentration of CA in a feeding artery. Some of the issues that may arise are: curve sampling at too low temporal resolution for the rapidly changing curve; errors in the peak height due to signal saturation at high CA concentrations; incomplete spoiling; partial volume and inflow effects; and motion.
Previous investigators have used consensus or population-based arterial input functions (AIFs) in the analysis of extended dynamic contrast-enhanced MR data. However it is not known whether population-based AIFs are also useful in perfusion imaging based on first-pass DCEMRI.
In this work, we explore the possibility of extracting a population AIF for lung perfusion imaging, detailing the first pass of the CA bolus at high temporal resolution in the pulmonary arteries (PA). The results of the analysis using a measured AIF and the population AIF are compared.
A population AIF was obtained from the PA. While there is significant variation among the GV fitting from which the population AIF was obtained, the variation is not related to dose but the AUC is linearly related to dose. When comparing the results of the perfusion analysis within our patient population, the only significant difference was observed in in BV, which is lower when using a population AIF. This is probably due to some of the measured AIF presenting too low AUC.
In this work, we have derived a population AIF for perfusion quantification in the lung. This AIF may be of use in settings where measured AIF quality is insufficient to allow reliable quantification.