Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow

Abstract

Predictive modeling of hyperemic coronary and myocardial blood flow (MBF) greatly supports diagnosis and prognostic stratification of patients suffering from coronary artery disease (CAD). In this work, we propose a novel strategy, using only readily available clinical data, to build personalized inlet conditions for coronary and MBF models and to achieve an effective calibration for their predictive application to real clinical cases. Experimental data are used to build personalized pressure waveforms at the aortic root, representative of the hyperemic state and adapted to surrogate the systolic contraction, to be used in computational fluid-dynamics analyses. Model calibration to simulate hyperemic flow is performed in a "blinded" way, not requiring any additional exam. Coronary and myocardial flow simulations are performed in eight patients with different clinical conditions to predict FFR and MBF. Realistic pressure waveforms are recovered for all the patients. Consistent pressure distribution, blood velocities in the large arteries, and distribution of MBF in the healthy myocardium are obtained. FFR results show great accuracy with a per-vessel sensitivity and specificity of 100% according to clinical threshold values. Mean MBF shows good agreement with values from stress-CTP, with lower values in patients with diagnosed perfusion defects. The proposed methodology allows us to quantitatively predict FFR and MBF, by the exclusive use of standard measures easily obtainable in a clinical context. This represents a fundamental step to avoid catheter-based exams and stress tests in CAD diagnosis.

Keywords: Computational modeling; Coronary artery disease; Coronary pressure; Fractional flow reserve; Myocardial blood flow; Myocardial perfusion.

Fig. 1

Characteristic pressure waveform at the aortic root with the proposed 4-phases subdivision, key time instants and corresponding pressure values

Fig. 2

a, b Segmented domains and meshes for patient P7 used for the simulations of 3D blood fluid dynamics (a) and multicompartment Darcy (b) problems. c Landmarks on the coronary tree used for the computation of FFR values for patient P2 (shown as an example of left coronary dominance). Notice that the landmark for RCA is placed earlier than the interventricular and posterior branches, and the landmark for LCX is placed earlier than the posterolateral descent

Fig. 3

a Regression line built on clinical data of rest and stress heart rate measures. b, c Regression lines built on clinical data of rest and stress systolic/diastolic pressure measures

Fig. 4

Aortic root $P_{\text{ar}}$ and effective pressure $P_{\text{eff}}$ curves reconstructed with the 4-phases parametrization technique described in “Methods” section for the patients P1–P8

Fig. 5

a Pressure field in the large coronaries computed at peak diastolic pressure ($t=0.3$ s). b Velocity field computed at mid diastole ($t=0.5$ s) in the LAD; the slicing plane is aligned with the LAD centerline in the middle segment but not at the inlet. c Left/right coronary blood flow Q computed over time at the left/right coronary inlets. d Istantaneous myocardial blood flow (MBF) in the left ventricle free wall computed at mid diastole ($t=0.5$ s). Patient P1

Fig. 6

$FF{R}_{\text{CT}}$ results for patients P1–P8, computed over the whole coronary domain. Patients 5–8 have at least one major artery with positive outcome of the invasive FFR exam (invasive $FFR<0.8$)

Fig. 7

Quantitative comparison between computed $MB{F}_{\text{comp}}$ vs clinical $MB{F}_{\text{ctp}}$ values of myocardial blood flow in patients 1–8. Patients 4–8 have at least a principal perfusion territory with positive outcome of the stress-CTP exam ($MB{F}_{\text{ctp}}<150$ ml/min/100g).