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Multicenter Study
. 2025 Jul;18(7):e018188.
doi: 10.1161/CIRCIMAGING.124.018188. Epub 2025 May 13.

Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure

Waseem Hijazi  1 , Aakash Shanbhag  1   2 , Robert J H Miller  1   3 , Paul Kavanagh  1 , Aditya Killekar  1 , Mark Lemley  1 , Samuel Wopperer  4 , Stacey Knight  5 , Viet T Le  5 , Steve Mason  5 , Wanda Acampa  6 , Thomas Rosamond  7 , Damini Dey  1 , Daniel S Berman  1 , Panithaya Chareonthaitawee  4 , Marcelo F Di Carli  8   9 , Piotr J Slomka  1
Affiliations
Multicenter Study

Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure

Waseem Hijazi et al. Circ Cardiovasc Imaging. 2025 Jul.

Abstract

Background: Computed 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.

Methods: We 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.

Results: During 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.

Conclusions: Deep 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.

Keywords: artificial intelligence; deep learning; heart failure; myocardial perfusion imaging; positron emission tomography computed tomography.

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Conflict of interest statement

Dr Miller received grant support and consulting fees from Pfizer as well as grant support from Alberta Innovates. Dr Chareonthaitawee reports consulting for Clario, Ionetix, and Royalties from UpToDate. Dr Di Carli reports grant support from Gilead Sciences, in-kind research support from Amgen, and consulting fees from MedTrace. Drs Berman and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Berman served as a consultant for GE Healthcare. Dr Slomka received research grant support from Siemens Medical Systems, and consulting fees from Synektik S.A. The other authors report no conflicts.

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References

    1. Fihn SD, Gardin JM, Abrams J, Berra K, Blankenship JC, Dallas AP, Douglas P, Foody J, Gerber T, Hinderleter T, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. Circulation. 2012;126:e354–471. - PubMed
    1. Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med. 2024; 54: 648–657. - PubMed
    1. Miller RJH, Shanbhag A, Killekar A, Lemley M, Bednarski B, Kavanagh PB, Feher A, Miller E, Bateman T, Builoff V, Liang J, et al. AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovasc Imaging. 2024;17:780–91. - PMC - PubMed
    1. Miller RJH, Klein E, Gransar H, Slomka PJ, Friedman JD, Hayes S, Thomson L, Tamarappo B, Rozanski A, Berman D, et al. Prognostic significance of previous myocardial infarction and previous revascularization in patients undergoing SPECT MPI. Int J Cardiol. 2020;313:9–15. - PubMed
    1. Murthy VL, Bateman TM, Beanlands RS, Berman DS, Borges-Neto S, Chareonthaitawee P, Cerqueira M, deKemp R, DePuey G, Dilsizian V et al. Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC. J Nucl Med. 2018;59:273–93. - PubMed

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