Coronary Artery Disease and Epicardial Adipose Tissue

Epicardial adipose tissue (EAT) can locally affect the coronary arteries and play a significant role in the development and progression of coronary artery disease (CAD), as emerged only recently. The mechanisms through which epicardial fat can cause atherosclerosis are complex and multifactorial. Its anatomical proximity to the heart, the unique transcriptome, and intense proteasome are the major atherogenic factors of the epicardial adipose tissue. EAT can cause atherosclerosis via several mechanisms that could be summarized in inflammation, innate immunity, oxidative stress, lipotoxicity, and glucotoxicity. EAT, regardless of whether it is measured as volume or thickness, is higher in patients with CAD as compared to individuals without CAD. The more proximal EAT is to the coronary arteries, the higher is its inflammatory activity. EAT provides prognostic information and improves the prediction of first coronary events. EAT volume is greater in subjects with incident coronary heart disease. The incidence of fatal or nonfatal coronary event significantly increased with higher EAT and remains significant even after adjustment for coronary calcium calcification score and obesity. EAT is linked to early coronary plaque components and, therefore, plays a role in the early phases of asymptomatic atherosclerosis. Routine assessment of EAT could be implemented for a better prediction and stratification of CAD

Analytical Quantification of Aortic Valve 18F-Sodium Fluoride PET Uptake

BackgroundChallenges to cardiac PET-CT include patient motion, prolonged image acquisition and a reduction of counts due to gating. We compared two analytical tools, FusionQuant and OsiriX, for quantification of gated cardiac 18F-sodium fluoride (18F-fluoride) PET-CT imaging.MethodsTwenty-seven patients with aortic stenosis were included, 15 of whom underwent repeated imaging 4 weeks apart. Agreement between analytical tools and scan-rescan reproducibility was determined using the Bland-Altman method and Lin's concordance correlation coefficients (CCC).ResultsImage analysis was faster with FusionQuant [median time (IQR) 7:10 (6:40-8:20) minutes] compared with OsiriX [8:30 (8:00-10:10) minutes, p = .002]. Agreement of uptake measurements between programs was excellent, CCC = 0.972 (95% CI 0.949-0.995) for mean tissue-to-background ratio (TBRmean) and 0.981 (95% CI 0.965-0.997) for maximum tissue-to-background ratio (TBRmax). Mean noise decreased from 11.7% in the diastolic gate to 6.7% in motion-corrected images (p = .002); SNR increased from 25.41 to 41.13 (p = .0001). Aortic valve scan-rescan reproducibility for TBRmax was improved with FusionQuant using motion correction compared to OsiriX (error ± 36% vs ± 13%, p < .001) while reproducibility for TBRmean was similar (± 10% vs ± 8% p = .252).Conclusion18F-fluoride PET quantification with FusionQuant and OsiriX is comparable. FusionQuant with motion correction offers advantages with respect to analysis time and reproducibility of TBRmax values