Measurement uncertainty quantification for myocardial perfusion using cardiac positron emission tomography imaging

Abstract

Perfusion, the flow of blood, and hence oxygen, is essential to the functioning of the heart. Reduced perfusion (or ischemia), is a reliable indicator of the presence of significant obstructive coronary artery disease (CAD), which is one of the biggest causes of death in Europe. Myocardial perfusion imaging is a non-invasive technique used in the diagnosis, management and prognosis of CAD and is a key component in the triage of patients into treatment and non-treatment groups. Cardiac positron emission tomography (PET) is an imaging technique with high sensitivity and specificity to CAD, however perfusion measurements are difficult to calibrate against a common reference standard, and confidence in them is generally not quantified in terms of measurement uncertainty. There are a number of steps involved in measuring perfusion using cardiac PET—from patient preparation to data analysis—each associated with potential sources of uncertainty. The absence of measurement uncertainty quantification can lead to inaccuracies in measurement results, a lack of comparability between devices or scanning facilities, and is likely to be detrimental to a decision-making process. In this paper, we identify some of the sources of measurement uncertainty in the cardiac PET perfusion measurement pipeline. We assess their relative contribution by performing a sensitivity analysis using experimental data of a flow phantom acquired on a PET scanner. The results of this analysis will inform users of how parameter choices in their imaging pipeline affect the output of their measurements, and serves as a starting point to develop an uncertainty quantification method.