ArticleCirculation. Cardiovascular imaging2025
Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.
Article in Circulation. Cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Opportunistic CT Attenuation Biomarkers of Anemia Are Associated With Impaired Myocardial Flow Reserve and Cardiovascular Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Virtual myocardial blood flow and flow reserve from static PET imaging using artificial intelligence.medRxiv : the preprint server for health sciences · 2026Article
- The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with positron emission tomography (REFINE PET):Rationale and design.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Article
- Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.BMC medical informatics and decision making · 2025Article
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Abstract
backgroundComputed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization.
methodsWe included 18 079 patients with cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis.
resultsDuring a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19-1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23-1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20-1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23-1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18-1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0-1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6-1.17]) were independent predictors of reduced myocardial flow reserve.
conclusionsDeep learning-derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.
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