Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities

Abstract

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.

Keywords: blood flow; cardiovascular; computational fluid dynamics (CFD); coronary; intra-cardiac flow simulation; patient-specific.

Figure 1

Schematic drawings for the procedures of patient-specific simulations for blood flow in coronary arteries, including (1) acquisition of CTA or ICA images (2) segmentation of acquired images (3) reconstruction of 3D model (4) mesh generation to discretize the 3D model (5) solving the mass and momentum conservation equations to simulate the blood flow in coronary arteries, if FSI is taken into consideration, solid solver will also be activated, and (6) presentation of simulations.

Figure 2

Distributions of (A) P (Pressure), (B) WPG (wall pressure gradient), (C) WSS (wall shear stress), (D) OSI (oscillatory shear index), (E) RRT (relative residence time), and (F) SPA (stress phase angle) on the virtually healthy and diseased left coronary artery trees respectively. Labels of (A–D) indicate the stenosis locations. In the virtually healthy artery model, low WSS, and high RRT was exhibited in three of the four locations, where the stenoses were formed, and high WSS with low RRT was exhibited in the fourth. These findings suggest that coronary plaque is more likely to form in locations with low- WSS- and- high- RRT or high- WSS- and- low- RRT. From Zhang et al. (2015).

Figure 3

Diagrams and characteristics for calculating non-invasive FFR through combining CTCA with CFD by the companies of (A) Heart Flow: FFR_CT (Gaur et al., 2013); (B) Siemens (1st generation: cFFR) (Coenen et al., 2015); (C) Siemens (2nd generation: cFFR_ML) (Itu et al., 2016); and (D) Toshiba: CT-FFR (Ko et al., 2017).

Figure 4

Flow chart of CFD simulation of patient-specific intra-cardiac flow. Black box: each step of numerical simulation (Su et al., 2016). Red box: left ventricle reconstruction from MRI images. Green box: CFD mesh generation. Violet box: Mapping between reconstructed model and CFD surface mesh. Dotted blue box: CFD mesh resulted from mapping. Blue box: A series of CFD meshes at each frame. Elements (blue) and grids (green) are for reconstructed geometry and CFD mesh, respectively.

Figure 5

Post-processing of intra-cardiac flow showing (A) flow mapping (Seo et al., 2014); (B) vortex structure (Seo et al., 2014); (C) kinetic energy (Seo and Mittal, 2013); and (D) flow component (Svalbring et al., 2016).

Figure 6

CFD simulation of intra-cardiac flow showing (A) vortex formation in dilated cardiomyopathy (DCM) (Mangual et al., 2013); (B) the distribution of blood velocity before and after surgical ventricular restoration (SVR) (Khalafvand et al., 2014); (C) vortex formation in right ventricle (Mangual et al., 2012); (D) vortex formation in hypertrophic cardiomyopathy (HCM); (E) distribution of blood velocity in a single ventricle (SV) (SVC, superior vena cava; RPA, right pulmonary artery; LPA, left pulmonary artery); (F) the distribution of blood velocity in the LV with myocardial infarction (MI) (Khalafvand et al., 2012).