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Fractional Flow Reserve, Myocardial

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A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation.

Medical & biological engineering & computing
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...

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

AJR. American journal of roentgenology
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...

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
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...

Current Reimbursement Landscape of Artificial Intelligence.

Journal of the American College of Radiology : JACR
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
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...

Artificial intelligence in cardiac computed tomography.

Progress in cardiovascular diseases
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...

A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning-based FFR in the work-up of stable coronary artery disease.

European radiology
OBJECTIVES: To validate a novel stepwise strategy in which computed tomography-derived fractional flow reserve (FFR) is restricted to intermediate stenosis on coronary computed tomography angiography (CCTA) and computed tomography myocardial perfusio...

A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model.

Computers in biology and medicine
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...