Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.

Abstract

Increased right ventricular (RV) radiotracer uptake on perfusion imaging has been recognized as a marker of increased cardiovascular risk. However, this uptake is challenging to quantify because of the variable intensity of uptake in a thin structure. We used a validated artificial intelligence-enhanced method for segmenting the right ventricle from CT attenuation correction (CTAC) imaging to automatically quantify RV activity and then evaluated its prognostic significance. <b>Methods:</b> We evaluated consecutive patients from 11 sites who underwent PET myocardial perfusion imaging with available CTAC. We segmented the RV and left ventricular myocardium from CTAC images using deep learning and then quantified RV activity measures on coregistered PET images. We evaluated associations between RV activity measures and the incidence of death or myocardial infarction (MI). <b>Results:</b> In total, 25,444 patients were included in our analysis (median age, 67 y). During a median follow-up of 4.1 y, 6009 patients (23.6%) experienced death or MI. Most RV activity measures were associated with the risk of death or MI. Higher maximum RV rest activity was associated with an increased risk of death or MI (unadjusted hazard ratio, 1.17 per SD for ¹³N-ammonia and 1.19 per SD for ⁸²Rb). These associations persisted after adjusting for age, sex, medical history, perfusion, function, and myocardial flow reserve. <b>Conclusion:</b> Deep learning can extract RV activity from hybrid PET/CT myocardial perfusion imaging. These measures are associated with myocardial flow reserve and provide complementary information regarding cardiovascular risk.