Annals of Biomedical Engineering

, Volume 46, Issue 9, pp 1325–1336 | Cite as

A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials

  • Brett M. Burton
  • Kedar K. Aras
  • Wilson W. Good
  • Jess D. Tate
  • Brian Zenger
  • Rob S. MacLeod


The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease—inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm suggesting instead a more distributed pattern of tissue injury. These findings come from experiments and so have both the impact and the limitations of measurements from living organisms. Computer models have often been employed to overcome the constraints of experimental approaches and have a robust history in cardiac simulation. To this end, we have developed a computational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac potentials. To validate our framework, we simulated, visualized, and analyzed 226 experimentally derived acute myocardial ischemic events. Simulation outcomes agreed both qualitatively (feature comparison) and quantitatively (correlation, average error, and significance) with experimentally obtained epicardial measurements, particularly under conditions of elevated ischemic stress. Our simulation framework introduces a novel approach to incorporating subject-specific, geometric models and experimental results that are highly resolved in space and time into computational models. We propose this framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.


Ischemia ST deviation Computer model Cardiac simulation Electrocardiographic forward problem Extracellular potentials 







Left anterior descending coronary artery


Time point at QRS complex offset


Time point at T-wave peak


Time point 40% between \({\text{QRS}}_{{{\text{off}}}}\) and \({\text{T}}_{{{\text{peak}}}}\)


Fast imaging with steady-state precession


Fast low angle shot


Magnetic resonance imaging


Diffusion weighted magnetic resonance imaging


Pearson’s correlation coefficient


Root-mean-square error


Maximum absolute error value


Left ventricle


Right ventricle



Support for this research was provided by the Nora Eccles Treadwell Foundation and the Richard A. and Nora Eccles Harrison Fund for Cardiovascular Research. Additional support and resources were provided by the NIH/NIGMS Center of Integrative Biomedical Computing ( under Grant P41 GM103545-17.


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Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  • Brett M. Burton
    • 1
    • 2
    • 3
  • Kedar K. Aras
    • 4
  • Wilson W. Good
    • 1
    • 2
    • 3
  • Jess D. Tate
    • 1
    • 2
    • 3
  • Brian Zenger
    • 1
    • 2
    • 3
  • Rob S. MacLeod
    • 1
    • 2
    • 3
  1. 1.Department of BioengineeringUniversity of UtahSalt Lake CityUSA
  2. 2.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  3. 3.Cardiovascular Research & Training InstituteUniversity of UtahSalt Lake CityUSA
  4. 4.George Washington UniversityWashingtonUSA

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