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Prognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease.
J Am Heart Assoc
January 2025
Department of Cardiology Beijing Anzhen Hospital, Capital Medical University Beijing China.
Background: Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited.
Methods And Results: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated.
Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease.
Radiol Adv
October 2024
Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Purposes: The objective was to evaluate the accuracy of a novel CT dynamic angiographic imaging (CT-DAI) algorithm for rapid fractional flow reserve (FFR) measurement in patients with coronary artery disease (CAD).
Materials And Methods: This retrospective study included 14 patients (age 58.5 ± 10.
Automated stenosis estimation of coronary angiographies using end-to-end learning.
Int J Cardiovasc Imaging
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.
View Article and Find Full Text PDFToward non-clinical validation of fractional flow reserve simulation software: A generic coronary artery geometry.
J Biomech
December 2024
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, United States of America.
Medical image-based diagnostic techniques have become increasingly common in the clinic. Estimating fractional flow reserve in coronary stenoses from medical image data is among the most prominent examples. The modeling techniques used in these clinical tools require rigorous experimental validation yet there is currently no standardized, public toolset to help assess model credibility.
View Article and Find Full Text PDFBackground: White matter hyperintensities (WMHs) are areas of increased signal on T2‐weighted MRI scans. They vary in size, location, and intensity, suggesting different underlying conditions like small vessel disease and inflammation. This variation potentially links WMH to outcomes ranging from normal aging to severe neurological disorders.
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