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Fractional Flow Reserve

Abstract

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Machine Learning Approaches in Cardiovascular Imaging Long-term Variations of FFR and iFR After Transcatheter Aortic Valve Implantation Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps) Fractional Flow Reserve-Guided Complete Revascularization Improves the Prognosis in Patients With ST-Segment-Elevation Myocardial Infarction and Severe Nonculprit Disease: A DANAMI 3-PRIMULTI Substudy (Primary PCI in Patients With ST-Elevation Myocardial Infarction and Multivessel Disease: Treatment of Culprit Lesion Only or Complete Revascularization) Clinical Significance of Concordance or Discordance Between Fractional Flow Reserve and Coronary Flow Reserve for Coronary Physiological Indices, Microvascular Resistance, and Prognosis After Elective Percutaneous Coronary Intervention Five-Year Outcomes with PCI Guided by Fractional Flow Reserve

Original Research2017 Oct;10(10). p

JOURNAL:Circ Cardiovasc Imaging. Article Link

Machine Learning Approaches in Cardiovascular Imaging

Henglin M, Stein G, Hushcha PV et al. Keywords: algorithms; artificial intelligence; automation; workflow; flow reserve; CMR

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