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Computational fluid dynamics modelling in cardiovascular medicine
  1. Paul D Morris1,2,3,
  2. Andrew Narracott1,2,
  3. Hendrik von Tengg-Kobligk4,
  4. Daniel Alejandro Silva Soto1,2,
  5. Sarah Hsiao1,
  6. Angela Lungu1,2,
  7. Paul Evans1,2,
  8. Neil W Bressloff5,
  9. Patricia V Lawford1,2,
  10. D Rodney Hose1,2,
  11. Julian P Gunn1,2,3
  1. 1Department of Cardiovascular Science, University of Sheffield, Sheffield, UK
  2. 2Insigneo Institute for In Silico Medicine, Sheffield, UK
  3. 3Department of Cardiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
  4. 4University Institute for Diagnostic, Interventional and Pediatric Radiology, University Hospital of Bern, Inselspital, Bern, Switzerland
  5. 5Faculty of Engineering & the Environment, University of Southampton, Southampton, UK
  1. Correspondence to Dr Paul D Morris, Medical Physics Group, Department of Cardiovascular Science, University of Sheffield, The Medical School, Beech Hill Road, Sheffield S102RX, UK; paulmorris{at}


This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards ‘digital patient’ or ‘virtual physiological human’ representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See:

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