## 6 Discussion  
We appreciate that model clinical testing routinely acquires immense amounts of data specific to the subject/patient. This includes the special organ that is the heart. The pulsatility provided by the heart is important in measurements such as FFR. However, modelling is an essential complement of CTA-driven FFR. In addition, modelling is essential due to existing heterogeneity among clinical providers. The ready availability of high performance computing combined with high resolution clinical imaging modalities have augmented the application of computational fluid dynamics for in silico modelling and simulation-based investigation of complex biological processes [64]. However, due to the time and resource-intensive nature of large, multi-scale hemodynamic simulations, the clinical uptake of 3D modelling remains limited as it presently cannot be performed in real time. In contrast, the predictive capability of reduced order surrogates such as 1D and lumped parameter models have shown promise in their reliability relative to 3D models [17, 56]. The wide use and reputation of FFR as the gold standard for coronary artery disease diagnosis motivates an investigation into the factors affecting FFR. Bearing in mind the utility and credibility of reduced order models for CFD simulation, a lumped parameter model of the human coronary vasculature [27] was further developed in this study. The model is capable of personalization based on clinical measurements of aortic pressure waves, imaging based vascular geometry (lengths, radii, and morphometry), as well as cardiac wall motion kinematics [65]. As such, the model permits imaging-clinical data assessment as a computationally efficient instrument, prior to detailed 3D computational fluid dynamics simulations. Novel imaging protocols that account for cardiac chamber to chamber diastole will further fortify refinement of the diagnostic instrument. This theoretical study illuminates the relative relevance of focal and diffuse stenosis. It also suggests that knowledge of co-morbidities will improve our clinical diagnostics. Furthermore, it informs our upcoming 3D investigation regarding the clinical data that will permit both validation as well as prediction. Study design: J.J.J., S.R.K. and C.W.M. ; model development, T.-Y.L. provided the CTA data; J.J.J. ; simulations: J.J.J. and C.S. ; data analysis and figures, J.J.J. ; draft writing, J.J.J. and S.R.K. ; final manuscript, S.R.K., J.J.J., C.S., T.-Y.L., D.G. and C.W.M. ; funding secured by S.R.K., D.G. and C.W.M. All authors have read and agreed to the published version of the manuscript. This research was funded by Canada Canarie Inc. (RS3-111), Canadian NSERC grant (R4081A03) and Canada Heart and Stroke Foundation (G-20-0028717). This study did not use sensitive data, patient information, or any other identity data. This computational study did not require ethics approvals of any nature. This computational study did not require consent from patients or otherwise. The authors agree to make the model data openly available upon the publication of the manuscript. The model code is available in our laboratory's GITHUB, available online (accessed on 25 February 2022): [[https://github.com/mccsssk2/MDPI2022_JermiahsCoronary](https://github.com/mccsssk2/MDPI2022_JermiahsCoronary)]([https://github.com/mccsssk2/MDPI2022_JermiahsCoronary](https://github.com/mccsssk2/MDPI2022_JermiahsCoronary)). This work was supported by Canada Canarie Inc. (project: RS3-111), partly by Canadian Heart and Stroke Foundation grant (G-20-0028717, PI: CWM), and partly by Canadian NSERC grant (R4081A03, PI: DG). This work is part of [PERSON]'s research-based undergraduate training in Western University. SRK also thanks Lawson Health Research Institute, Western University, MITACS Globalink (Canada), and Compute Canada for funding, compute resources, labor, and other resources. We thank the FIMH 2021 organizers for the opportunity to disseminate our work.