A minimal factor overlap method for resolving ambiguity in factor analysis of dynamic cardiac PET

Factor analysis has been pursued as a means to decompose dynamic cardiac PET images into different tissue types based on their unique physiology. Each tissue is represented by a time-activity profile (factor) and an associated spatial distribution (structure). Decomposition is based on non-negative constraints of both the factors and structures; however, additional constraints are required to achieve a unique solution. In this work we present a novel method (minimal factor overlap - MFO) and compare its performance to a previously publishe

Intra-and inter-operator repeatability of myocardial blood flow and myocardial flow reserve measurements using rubidium-82 pet and a highly automated analysis program

Background: Changes in myocardial blood flow between rest and stress states are commonly used to diagnose coronary artery disease. Relative myocardial perfusion imaging (MPI) is used routinely while myocardial blood flow quantification (MBF) may improve the sensitivity for detection of early disease. The ratio of flow at stress and rest (S/R) and their difference (S-R) have both been proposed as a means to detect regions with reduced myocardial flow reserve (MFR). In this study, we describe a highly automated method to calculate regional and global rest, stress, S/R, and S-R polar maps of the left ventricle myocardium. Methods: We measured the inter-and intra-operator variability using two randomized datasets (n = 30 each) for each of two operators (novice and expert) with correlation and Bland-Altman reproducibility coefficient (RPC%) analyses. Results: S-R MBF had less inter-operator dependent variability than S/R (RPC% = 5.0% vs 12.6%, P <.001). While there was no difference in intra-operator variability with S-R MBF (novice vs expert RPC% = 6.4% vs 5.9%, P = ns), variability was higher in the noviceoperator for S/R (RPC% = 16.8% vs 8.5% respectively, P <.001), suggesting that S-R may be preferred for detecting small changes in MFR. The novice operator's intervention pattern became more similar to that of the expert in the later dataset, emphasizing the need for adequate training and quality assurance. Conclusion: The proposed method results in low operator-dependent variability, suitable for routine use. Copyrigh