Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.
Authors
Affiliations (17)
Affiliations (17)
- Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California.
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada.
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
- Cardiology Division, Montefiore Health System and Albert Einstein College of Medicine, Bronx, New York.
- Department of Radiology (Nuclear Medicine), Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York.
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California.
- Houston Methodist DeBakey Heart and Vascular Center, Houston Methodist Academic Institute, Houston, Texas.
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York.
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, Utah.
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
- Department of Cardiovascular Medicine, University of Kansas Medical Center, Kansas City, Kansas; and.
- Cardiovascular Imaging Program, Departments of Radiology and Medicine; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology; and Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
- Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California; [email protected].
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 <sup>13</sup>N-ammonia and 1.19 per SD for <sup>82</sup>Rb). 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.