Myocardial Perfusion Scintigraphy With Adenosine

Brinkman, Niels; Dibbets‐Schneider, Petra; Scholte, Arthur J.; Stokkel, Marcel P. M.

doi:10.1097/rlu.0b013e31815f2454

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The exact mechanism involved in this phenomenon is still unknown and has been reported before [30], but regarding the data presented in the tables, it seems to be related to a slight increase in ESV. Whether this is a direct effect of adenosine or is related to other factors has to be studied.…”

Section: Discussionmentioning

confidence: 66%

The consequences of a new software package for the quantification of gated-SPECT myocardial perfusion studies

Veen

Scholte

Dibbets‐Schneider

et al. 2010

Eur J Nucl Med Mol Imaging

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PurposeSemiquantitative analysis of myocardial perfusion scintigraphy (MPS) has reduced inter- and intraobserver variability, and enables researchers to compare parameters in the same patient over time, or between groups of patients. There are several software packages available that are designed to process MPS data and quantify parameters. In this study the performances of two systems, quantitative gated SPECT (QGS) and 4D-MSPECT, in the processing of clinical patient data and phantom data were compared.MethodsThe clinical MPS data of 148 consecutive patients were analysed using QGS and 4D-MSPECT to determine the end-diastolic volume, end-systolic volume and left ventricular ejection fraction. Patients were divided into groups based on gender, body mass index, heart size, stressor type and defect type. The AGATE dynamic heart phantom was used to provide reference values for the left ventricular ejection fraction.ResultsAlthough the correlations were excellent (correlation coefficients 0.886 to 0.980) for all parameters, significant differences (p < 0.001) were found between the systems. Bland-Altman plots indicated that 4D-MSPECT provided overall higher values of all parameters than QGS. These differences between the systems were not significant in patients with a small heart (end-diastolic volume <70 ml). Other clinical factors had no direct influence on the relationship. Additionally, the phantom data indicated good linear responses of both systems.ConclusionThe discrepancies between these software packages were clinically relevant, and influenced by heart size. The possibility of such discrepancies should be taken into account when a new quantitative software system is introduced, or when multiple software systems are used in the same institution.

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Section: Discussionmentioning

confidence: 66%