Partarrieu, I X; Jagan, K; Fenwick, A; Han, C; Siekkinen, R; Teuho, J; Saraste, A; Smith, N A S (2022) Measurement uncertainty quantification for myocardial perfusion using cardiac positron emission tomography imaging. Measurement Science and Technology, 33 (6). 064002

Preview

Text eid9408.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

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 versus 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 methods.

Item Type:	Article
Subjects:	Mathematics and Scientific Computing > Measurement Uncertainties
Divisions:	Data Science
Identification number/DOI:	10.1088/1361-6501/ac58e3
Last Modified:	07 Apr 2022 08:12
URI:	https://eprintspublications.npl.co.uk/id/eprint/9408