AIMC Topic: Fractional Flow Reserve, Myocardial
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A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model.
Computers in biology and medicine
Jan 3, 2024
The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards non-invasive prediction of FFR. Although the non-invasive derivation of FFR (FFR) using computational fluid dynamics (CFD) principles has becom...
Artificial intelligence in cardiac computed tomography.
Progress in cardiovascular diseases
Sep 7, 2023
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applicatio...
Current Reimbursement Landscape of Artificial Intelligence.
Journal of the American College of Radiology : JACR
Aug 19, 2023
One of the biggest hurdles to widespread adoption of new procedures and technology such as artificial intelligence (AI) algorithms is payment and coverage policy. Noninvasive assessment of coronary fractional flow reserve is one AI imaging algorithm ...
Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.
Heart and vessels
Aug 8, 2023
Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accur...
Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.
Journal of applied clinical medical physics
Jul 24, 2023
PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological...
Editorial Comment: On-Site Deep Learning-Based FFR-CT-A Novel Method to Evaluate Functionally Significant Stenosis.
AJR. American journal of roentgenology
May 3, 2023
High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.
AJR. American journal of roentgenology
May 3, 2023
Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer...
Computed Tomography Angiography
Coronary Stenosis
Fractional Flow Reserve, Myocardial
Coronary Artery Disease
Reference Standards
Male
Middle Aged
Deep Learning
Predictive Value of Tests
Coronary Angiography
Constriction, Pathologic
Female
Reproducibility of Results
Humans
Aged
Algorithms
Retrospective Studies
A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation.
Medical & biological engineering & computing
Feb 11, 2023
Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the di...
Deep learning-based prediction of coronary artery stenosis resistance.
American journal of physiology. Heart and circulatory physiology
Oct 21, 2022
Coronary artery stenosis resistance (SR) is a key factor for noninvasive calculations of fractional flow reserve derived from coronary CT angiography (FFR). Existing computational fluid dynamics (CFD) methods, including three-dimensional (3-D) comput...
Performance evaluation of deep learning-based post-processing and diagnostic reporting system for coronary CT angiography: a clinical comparative study.
Chinese medical journal
Oct 5, 2022
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