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Updates on Imaging Modalities for the Diagnosis of Aortic Stenosis.

August 20, 2026pubmed logopapers

Authors

Itani H,Moumneh MB,Zayed A,Pradeep AG,Ennab M,Abofrekha B,Khaya O,Spagnola J,Frishman WH,Aronow WS

Affiliations (3)

  • From the Department of Internal Medicine.
  • Department of Cardiology, Staten Island University Hospital, Northwell Health, New Hyde Park, NY.
  • Department of Medicine, Westchester Medical Center and New York Medical College, Valhalla, NY.

Abstract

Aortic stenosis (AS) is the most common degenerative valvular disease in elderly patients and is linked to high morbidity and mortality. Accurate diagnosis and risk stratification are critical for effective management. Transthoracic echocardiography is the standard diagnostic tool, but its reliance on flow-dependent parameters can lead to inconsistent grading, especially in low-flow, low-gradient, or normal-flow, low-gradient AS. Advanced echocardiographic methods, such as 3D imaging, stress echocardiography, and Doppler indices, such as the mean gradient-to-effective orifice area ratio, improve the evaluation of AS severity and assist in clinical decision-making. Computed tomography provides a flow-independent evaluation of AS. It uses noncontrast calcium scoring with sex-specific thresholds, along with contrast-enhanced angiography, for detailed anatomical assessment. These modalities are essential for procedural planning, particularly for transcatheter aortic valve replacement. Cardiac magnetic resonance (CMR) provides additional prognostic information. It quantifies myocardial remodeling and fibrosis, which are associated with outcomes and recovery potential. Emerging technologies are expanding diagnostic capabilities in AS. Examples include 18F-sodium fluoride positron emission tomography for detecting microcalcification, artificial intelligence-based ECG and echocardiography for early diagnosis, and 4D flow CMR. Integration of echocardiography, computed tomography, CMR, and emerging positron emission tomography and artificial intelligence-based approaches can help address diagnostic uncertainty. This integration helps refine AS subtype classification and inform individualized intervention strategies.

Topics

Journal Article

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