Leiden University Scholarly Publications

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Nicol, E.D.; Norgaard, B.L.; Blanke, P.; Ahmadi, A.; Weir-McCall, J.; Horvat, P.M.; ... ; Leipsic, J. (2019)

The Future of Cardiovascular Computed Tomography Advanced Analytics and Clinical Insights

Article / Letter to editor
Cardiovascular computed tomography (CCT) has undergone rapid maturation over the last decade and is now of proven clinical utility in the diagnosis and management of coronary artery disease, in guiding structural heart disease intervention, and in the diagnosis and treatment of congenital heart disease. The next decade will undoubtedly witness further advances in hardware and advanced analytics that will potentially see an increasingly core role for CCT at the center of clinical cardiovascular practice. In coronary artery disease assessment this may be via improved hemodynamic adjudication, and shear stress analysis using computational flow dynamics, more accurate and robust plaque characterization with spectral or photon- counting CT, or advanced quantification of CT data via artificial intelligence, machine learning, and radiomics. In structural heart disease, CCT is already pivotal to procedural planning with adjudication of gradients before and following intervention, whereas in...Show moreCardiovascular computed tomography (CCT) has undergone rapid maturation over the last decade and is now of proven clinical utility in the diagnosis and management of coronary artery disease, in guiding structural heart disease intervention, and in the diagnosis and treatment of congenital heart disease. The next decade will undoubtedly witness further advances in hardware and advanced analytics that will potentially see an increasingly core role for CCT at the center of clinical cardiovascular practice. In coronary artery disease assessment this may be via improved hemodynamic adjudication, and shear stress analysis using computational flow dynamics, more accurate and robust plaque characterization with spectral or photon- counting CT, or advanced quantification of CT data via artificial intelligence, machine learning, and radiomics. In structural heart disease, CCT is already pivotal to procedural planning with adjudication of gradients before and following intervention, whereas in congenital heart disease CCT is already used to support clinical decision making from neonates to adults, often with minimal radiation dose. In both these areas the role of computational flow dynamics, advanced tissue printing, and image modelling has the potential to revolutionize the way these complex conditions are managed, and CCT is likely to become an increasingly critical enabler across the whole advancing field of cardiovascular medicine. (c) 2019 by the American College of Cardiology Foundation.Show less
atherosclerosis
cardiac CT
FFRCT
machine learning
radiomics
TMVR
All authors
Nicol, E.D.; Norgaard, B.L.; Blanke, P.; Ahmadi, A.; Weir-McCall, J.; Horvat, P.M.; Han, K.; Bax, J.J.; Leipsic, J.
Date
2019-06-01
Journal
JACC: Cardiovascular Imaging
Volume
12
Issue
6
Pages
1058 - 1072
DOI
doi:10.1016/j.jcmg.2018.11.037