Challenges of gated myocardial perfusion SPECT processing

The paper provides an in-depth look at gated myocardial perfusion single photon emission computed tomography (SPECT) data processing. Attention paid to several unmentioned subjects of the quantitative analysis of gated myocardial perfusion SPECT data. The article considers several options in the construction process of the ellipsoid coordinate system of the left ventricle (LV). Mathematical representation of polar maps is given. Formulas of the regional parameters calculation are proposed. Issues of phase analysis are explored.

Relation of glycosylated hemoglobin (HBA1C) to perfusion and function parameters of gated myocardial perfusion SPECT imaging

Funding Acknowledgements Type of funding sources: None. Aim To assess the relation of HBA1C to the various perfusion and function parameters derived from gated myocardial perfusion SPECT imaging (GMPS). Material and Methods We retrospectively reviewed 200 patients who had gated myocardial perfusion SPECT studies. Data collected included patients’ demographics, clinical data and lab findings (HTN, smoking, dyspnea, chest pain, DM, HA1C, total cholesterol, HDL and LDL), perfusion parameters (SSS, SRS, SDS and TPD), function and gated parameters (EF, EDV, ESV and wall motion abnormalities (WMA)). We used unpaired student T-test to compare mean values of continuous variables. ROC analysis was used to define the cutoff values for HA1C that best identifies patients with abnormal GMPS parameters. Chi square test was used to compare difference in frequency between categorical variables. Pearson correlation was used to assess the correlation between continuous variables. Results The study population included 200 patients (mean age of 58.21 ± 11.53 years; 102 (51%) males). Study included 132 (66%) diabetic patients. The mean HBA1C in diabetic patients was significantly higher compared to non-diabetic group (7.92 ± 1.99 vs 6.05 ± 0.99; p < 0.001). Male patients had higher level of HBA1C than females (7.57 ± 1.97 vs 6.99 ± 1.87; p = 0.034). The HBA1C% was negatively correlated to EF% and HDL (r= -0.262; p < 0.001 and r= -0.316; p < 0.001 respectively) while it was positively correlated to EDV and ESV (r= 0.291; p < 0.001 and r= 0.221; p = 0.002 respectively). The mean EF% and HDL were significantly lower in patients with HGA1C% > 6.5 (53.17 ± 14.55 vs 57.8 ± 12.61; p = 0.017) and (1.046 ± 0.262 vs 1.196 ± 0.295; p < 0.001). Patients with HGA1C% > 6.5 had more frequency of EF <50% (30.0% vs 15.6%; p = 0.017), more incidence of WMA (24.5% vs 12.2%; p = 0.027) and more ESV >44 ml (38.2% vs 20.0%; p = 0.005). Patients with HGA1C% > 6.5 had more prevalence of hypertension (77.3% vs 54.4%; p = 0.001) and more frequency of dyspnea (27.3% vs 15.6%; p = 0.047); however, with less prevalence of chest pain (70.9% vs 83.3%; p = 0.039). In diabetic patients subgroup again there was lower mean EF% and HDL in patients with HGA1C% > 7.5 (52.0 ± 14.59 vs 57.6 ± 11.55; p = 0.018 and 1.005 ± 0.239 vs 1.148 ± 0.273; p < 0.002 respectively). Also HGA1C% > 7.5 revealed more frequent EF <50% (33.3% vs 14.5%; p = 0.011), WMA (30.2% vs 11.6%; p = 0.008), more ESV >44 ml (41.3% vs 20.3%; p = 0.009) and in addition more EDV >100 ml (34.9% vs 18.8%; p = 0.037). No statistically significant relation could be found between HBA1C% and GMPS perfusion parameters including SSS, SRS, SDS and TPD%. Conclusion The HBA1C% was significantly associated with multiple GMPS function parameters abnormalities including lower EF, more WMA and larger ESV. The same association was noted in diabetic patient population in addition to larger EDV. Unfortunately in our limited study population with mixed diabetic and non-diabetic patients, no significant association was found between HBA1C% and GMPS perfusion parameters.