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Prog Biophys Mol Biol. 2014 Aug;115(2-3):198-212. doi: 10.1016/j.pbiomolbio.2014.08.005. Epub 2014 Aug 10.

Images as drivers of progress in cardiac computational modelling.

Author information

  • 1Dept. Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College of London, London, United Kingdom; Dept. Computer Science, University of Oxford, Oxford, United Kingdom. Electronic address: Pablo.Lamata@kcl.ac.uk.
  • 2Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
  • 3Dept. Computer Science, University of Oxford, Oxford, United Kingdom.
  • 4Dept. Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College of London, London, United Kingdom.
  • 5Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom.
  • 6National Heart and Lung Institute, Imperial College, London, United Kingdom; Dept. Computer Science, University of Oxford, Oxford, United Kingdom.

Abstract

Computational models have become a fundamental tool in cardiac research. Models are evolving to cover multiple scales and physical mechanisms. They are moving towards mechanistic descriptions of personalised structure and function, including effects of natural variability. These developments are underpinned to a large extent by advances in imaging technologies. This article reviews how novel imaging technologies, or the innovative use and extension of established ones, integrate with computational models and drive novel insights into cardiac biophysics. In terms of structural characterization, we discuss how imaging is allowing a wide range of scales to be considered, from cellular levels to whole organs. We analyse how the evolution from structural to functional imaging is opening new avenues for computational models, and in this respect we review methods for measurement of electrical activity, mechanics and flow. Finally, we consider ways in which combined imaging and modelling research is likely to continue advancing cardiac research, and identify some of the main challenges that remain to be solved.

Copyright © 2014 Elsevier Ltd. All rights reserved.

KEYWORDS:

Computational cardiac physiology; Medical imaging

PMID:
25117497
[PubMed - indexed for MEDLINE]
PMCID:
PMC4210662
Free PMC Article
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