<i>Radiology: Cardiothoracic Imaging</i> Highlights 2024.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611.
- Department of Cardiovascular Radiology & Endovascular Interventions, All India Institute of Medical Sciences, New Delhi, India.
- Department of Radiology, Medical University of South Carolina, Charleston, SC.
- Department of Radiology, The Ottawa Hospital, University of Ottawa, Ottawa, Canada.
- Apollo Hospitals, Chennai, India.
- Department of Radiology, University of Washington School of Medicine, Seattle, Wash.
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
- Department of Radiology, Mayo Clinic, Jacksonville, Fla.
- Department of Medical Imaging, Toronto General Hospital, University of Toronto, Toronto, Canada.
Abstract
<i>Radiology: Cardiothoracic Imaging</i> publishes research, technical developments, and reviews related to cardiac, vascular, and thoracic imaging. The current review article, led by the <i>Radiology: Cardiothoracic Imaging</i> trainee editorial board, highlights the most impactful articles published in the journal between November 2023 and October 2024. The review encompasses various aspects of cardiac, vascular, and thoracic imaging related to coronary artery disease, cardiac MRI, valvular imaging, congenital and inherited heart diseases, thoracic imaging, lung cancer, artificial intelligence, and health services research. Key highlights include the role of CT fractional flow reserve analysis to guide patient management, the role of MRI elastography in identifying age-related myocardial stiffness associated with increased risk of heart failure, review of MRI in patients with cardiovascular implantable electronic devices and fractured or abandoned leads, imaging of mitral annular disjunction, specificity of the Lung Imaging Reporting and Data System version 2022 for detecting malignant airway nodules, and a radiomics-based reinforcement learning model to analyze serial low-dose CT scans in lung cancer screening. Ongoing research and future directions include artificial intelligence tools for applications such as plaque quantification using coronary CT angiography and growing understanding of the interconnectedness of environmental sustainability and cardiovascular imaging. <b>Keywords:</b> CT, MRI, CT-Coronary Angiography, Cardiac, Pulmonary, Coronary Arteries, Heart, Lung, Mediastinum, Mitral Valve, Aortic Valve, Artificial Intelligence © RSNA, 2025.