Lindsay M. Quandt1* and Cyrus A. Raji2
1CereHealth Corporation, Littleton, Colorado, 80120, USA
2Mallinckrodt Institute of Technology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
Aim: Quantitative analysis of brain SPECT perfusion imaging is dependent on comparison to normative datasets that are challenging to produce. This study investigated the combination of SPECT neuroimaging from a large clinical population rather than small numbers of controls. We hypothesized this “population template” would demonstrate non-inferiority to a control dataset, providing a viable alternative for quantifying brain perfusion abnormalities in SPECT neuroimaging.
Methods: A total of 2,068 clinical SPECT scans were processed and averaged to form the “population template.” Validation was three-fold. First, the template was imported into SPECT brain analysis software, MIMneuro®, and compared against its control dataset of 90 individuals through its region and cluster analysis tools. Second, a cohort of 100 cognitively impaired subjects were evaluated against both the population template and MIMneuro®’s normative dataset to compute region-based metrics. Concordance and intraclass correlation coefficients, mean squared deviations, total deviation indices, and limits of agreement were derived from these data to measure agreement and test for non-inferiority. Finally, the same patients were clinically read in CereMetrix® to confirm that expected perfusion patterns appeared after comparison to the template.
Results: MIMneuro®’s default threshold for normality is ±1.65 z-score and this served as our non-inferiority margin. Direct comparison of the template to controls produced no regions that exceeded this threshold and all clusters identified were far from statistically significant. Agreement measures revealed consistency between the softwares and that CereMetrix® results were not inferior to MIMneuro®, albeit with proportional bias. Visual analysis also confirmed that expected perfusion patterns appeared when individual scans were compared to the population template within CereMetrix®.
Conclusions: We demonstrated a population template was not inferior to a smaller control dataset despite inclusion of abnormal scans. This suggests that our patient-based population template can serve as an alternative for identifying and quantifying perfusion abnormalities in brain SPECT.