In spite of the significant improvements in medical imaging and other diagnostic modalities, the incidence of premature morbidity and mortality for Coronary Artery Disease (CAD) patients is still very high, the main reason being the lack of accurate in-vivo and in-vitro patient-specific estimates for diagnosis and progression of the disease. For example, in the case of coronary stenosis, accurate estimates of the anatomy (amount of narrowing blockage in the coronary) as seen in the diagnostic images, can vastly underestimate or overestimate the severity of the blockage. For a functional assessment of such a blockage, analysis of the subsequent disease progression, and assessment of the best intervention/surgical option for an individual patient, it is important to incorporate multi-faceted information from the hemodynamics and cellular mechanisms from multiple scales. Incorporating such multi-scale information in a complex computational model has been difficult in the past due to the high computational demands. Therefore it is important to enable such simulations on High Performance Computing platforms.

The main objectives of HEART are:

• To develop, integrate and validate patient-specific multi-scale computational models with high predictive power for coronary circulation in healthy and diseased vessels:
  • Comprehensive modeling of the anatomical, hemodynamic, and cellular phenomena in the coronary circulation
  • Efficient multi-scale coupling with the state-of-the-art heart models for advanced patient-specific simulations
  • Assessment of functional parameters and subsequent validation of the models
• To improve the clinical management of coronary artery disease by leveraging the computational models to create specific therapeutic interventions:
  • Simulation based methods for intervention planning (virtual stenting, angioplasty and Coronary Artery Bypass Graft (CABG) ) using computational models
  • High Performance Computing architecture for efficiently addressing the multi-scale complexity, a critical requirement for translation into clinical decision making

Such an approach will result in a predictive comprehensive multi-scale model, which will not only be used for analyzing anatomical and functional aspects of coronary artery disease, but will also enable improved clinical management – both for diagnosis, and intervention planning.

Overall, our proposed approach, addressing therapeutic optimization and disease progression will significantly decrease morbidity, mortality and reduce costs for coronary artery disease treatment.