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Updated: May 2, 2026
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
Published on: December 16, 2022
Impact of software tools and kinetic model selection on myocardial blood flow and flow reserve quantitation in
Naochika Akiya1, Kenta Miwa1,2, Akira Ando3
1Department of Radiological Sciences, Graduate School of Health Sciences, Fukushima Medical University, Fukushima-shi, Fukushima, Japan.
Background:
Quantitative assessment of myocardial perfusion using 13N-ammonia PET with compartmental modeling enables evaluation of myocardial flow reserve (MFR) and prediction of patient prognosis. However, the reliability of these assessments can depend on the analytical methods used for quantitation.
Purpose:
The present study aimed to evaluate the variability and agreement of values obtained using three quantitative software tools and to assess the impact of kinetic model selection on myocardial blood flow (MBF) and MFR estimates in a clinical setting.
Methods:
We analyzed 100 patients who underwent 13N-ammonia PET/CT, including 60 with normal perfusion and 20, 10, and 10 with single-, two-, and three-vessel disease, respectively. We derived MBF and MFR at global (entire left ventricle) and regional (coronary territories) levels and evaluated five analytical pipelines: SyngoMBF, QPET, and three implementations of PMOD tools (1-tissue compartment, Hutchins, and UCLA models).
Results:
MBF and MFR showed high correlations among the software tools, although stress MBF statistically differed between PMOD and QPET. Correlation coefficients between software tools ranged from 0.81 to 0.91 at the global level, and Bland-Altman analysis demonstrated overall agreement with residual variability. In contrast, MBF and MFR values varied depending on the compartment model. The UCLA model yielded the highest stress MBF and MFR, and correlation coefficients between models ranged from 0.43 to 0.99 at the global level. Although Bland-Altman analysis showed overall agreement, noticeable scatter persisted and the UCLA model exhibited a positive bias.
Conclusion:
Quantitative MBF and MFR estimates from 13N-ammonia PET show good overall agreement across commonly used software tools but remain strongly dependent on kinetic model selection. These findings indicate that quantitative results are not directly interchangeable across different software and modeling approaches, underscoring the importance of methodological consistency when interpreting myocardial perfusion PET in clinical practice.
