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209 The Development and Validation of an in silico Model of Coronary Arterial Physiology for Diagnostic Use
  1. Paul Morris,
  2. Iwona Zwierzak,
  3. Patricia Lawford,
  4. D Sir,
  5. Rodney Hose,
  6. Julian Gunn
  1. University of Sheffield


Background Coronary revascularisation guided by physiological assessment with fractional flow reserve (FFR) improves outcomes and expenditure. However, due to practical limitations, FFR is only used in <10% of percutaneous coronary interventions (PCI) and almost none of the 250,000 diagnostic coronary angiograms performed every year in the UK. We aimed to develop an in silico model of coronary physiology which computes FFR using computational fluid dynamics (CFD) with data from coronary angiography (CAG), which also computes real-time coronary flow.

Methods We studied 40 patients with stable coronary artery disease undergoing PCI. The in silico workflow reconstructed coronary geometry from CAG images into 3-D geometries using image registration and segmentation protocols. The 3-D domain was coupled to an algebraically encoded zero-D model representing coronary microvascular resistance. CFD simulated pressure and flow. Computed pressures were validated against patient data. Flow was computed from those pressures and validated in a benchtop flow circuit constructed from patient-specific 3-D printed coronary arterial models, a blood analogue, and patient-specific coronary flow patterns using a programmable pump. Pressure and flow were measured with clinical pressure and flow sensitive wires. To reduce computation time, a novel pseudo-transient CFD method was developed and investigated.

Results Using generic boundary conditions, the initial model computed ‘virtual’ FFR (vFFR) with ±0.06 error and diagnosed physiological lesion significance (FFR <0.80) with 97% accuracy. Ultra-precise, transient CFD took 24 h to compute, whereas the novel ‘pseudo-transient’ method reduced computation to <4 mins on a standard PC with no significant loss of accuracy. Using case-specific boundary conditions (based on invasive measurements) error was reduced to ±0.004 and more useful, artery-specific, patient averaged values yielded ±0.01 error. Coronary flow was computed in all cases and, with the 3-D printed arteries, we demonstrated that the in silico model computed reliable coronary flow with 5.3% error in 3 min.

Conclusions Our in silico model computes reliable intra-coronary physiology, including pressure and flow, in timescales that are practical for clinical use in the catheter laboratory. Virtual modelling of FFR and hyperaemic stenosis resistance (HSR) is now feasible, which may facilitate increased uptake and impact of physiologically-guided revascularisation planning for every patient undergoing CAG, rather than a small minority undergoing selective PCI. Improved accuracy will follow from better characterisation of the individual’s microvascular resistance.

coronary stenosis.

Abstract 209 Figure 1

A patient case (a) with pressure and flow simulation results (b and c) across a severe right

  • Coronary physiology
  • Computer modelling
  • Coronary artery disease

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