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.