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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.

Respiratory motion may cause significant image artefacts in positron emission tomography/computed tomography (PET/CT) imaging. This study introduces a new bioimpedance-based gating method for minimizing respiratory artefacts. The method was studied in 12 oncologic patients by evaluating the following three parameters: maximum metabolic activity of radiopharmaceutical accumulations, the size of these targets as well as their target-to-background ratio. The bioimpedance-gated images were compared with non-gated images and images that were gated with a reference method, chest wall motion monitoring by infrared camera. The bioimpedance method showed clear improvement as increased metabolic activity and decreased target volume compared to non-gated images and produced consistent results with the reference method. Thus, the method may have great potential in the future of respiratory gating in nuclear medicine imaging.