Using 4D Cardiovascular Magnetic Resonance Imaging to Validate Computational Fluid Dynamics: A Case Study

Abstract

Computational fluid dynamics (CFD) can have a complementary predictive role alongside the exquisite visualization capabilities of 4D cardiovascular magnetic resonance (CMR) imaging. In order to exploit these capabilities (e.g., for decision-making), it is necessary to validate computational models against real world data. In this study, we sought to acquire 4D CMR flow data in a controllable, experimental setup and use these data to validate a corresponding computational model. We applied this paradigm to a case of congenital heart disease, namely, transposition of the great arteries (TGA) repaired with arterial switch operation. For this purpose, a mock circulatory loop compatible with the CMR environment was constructed and two detailed aortic 3D models (i.e., one TGA case and one normal aortic anatomy) were tested under realistic hemodynamic conditions, acquiring 4D CMR flow. The same 3D domains were used for multi-scale CFD simulations, whereby the remainder of the mock circulatory system was appropriately summarized with a lumped parameter network. Boundary conditions of the simulations mirrored those measured in vitro. Results showed a very good quantitative agreement between experimental and computational models in terms of pressure (overall maximum % error = 4.4% aortic pressure in the control anatomy) and flow distribution data (overall maximum % error = 3.6% at the subclavian artery outlet of the TGA model). Very good qualitative agreement could also be appreciated in terms of streamlines, throughout the cardiac cycle. Additionally, velocity vectors in the ascending aorta revealed less symmetrical flow in the TGA model, which also exhibited higher wall shear stress in the anterior ascending aorta.

Keywords: cardiovascular magnetic resonance imaging; congenital heart disease; mock circulatory loop; rapid prototyping; validation.

Figure 1

Both control (a) and TGA (b) anatomies are generated from CMR data (center). The exact same 3D volumes are manufactured with rapid prototyping (left) and meshed for computational simulations (right). Please note typical features of TGA anatomy, particularly the enlarged aortic root with a visible protrusion. AAo = ascending aorta; DAo = descending aorta; MPA = main pulmonary artery.

Figure 2

(A) Arrangement of compliant (C) and resistive (R) elements in the in vitro setup, as air chambers and metered taps, respectively, also showing the 3D rapid-prototyped model (TGA patient in the picture), the pump, and the atrial chamber implementing atrial pressure (P_atrium). (B) Arrangement for corresponding multi-scale CFD simulation, including 3D volume of TGA patient, coupled with lumped parameter network summarizing the remainder of the system. Each outlet is simulated with a non-linear and linear resistor in series. C_i/R_i = compliance/resistance for innominate artery; C_c/R_c = compliance/resistance for carotid artery; C_s/R_s = compliance/resistance for subclavian artery; C_d/R_d = compliance/resistance for descending aorta; C_t/R_t = terminal compliance/resistance; P_atrium = atrial pressure.

Figure 3

Summary of pressure data showing the realistic shape and range of the pressure waveform gathered in the TGA model (top panel) and in the control model (bottom panel). In vitro (blue) and in silico (red) signals are superimposed.

Figure 4

Comparison of flow streamlines in the TGA 3D model, 4D CMR data (left) and CFD results (right), at four different time points in the cardiac cycle.

Figure 5

Comparison of flow streamlines in the control 3D model, 4D CMR data (left) and CFD results (right), at four different time points in the cardiac cycle.

Figure 6

Correlation and Bland Altman plots for flow measurements (CMR vs. CFD) at four locations in the control model.

Figure 7

Correlation and Bland Altman plots for flow measurements (CMR vs. CFD) at four locations in the TGA model.

Figure 8

Comparison of velocity vectors at three cross-sections along the aorta in both TGA (left) and control (right) anatomies. The corresponding velocity data at the same planes extracted from the experimental 4D CMR are shown next to each model, showing good agreement on the same velocity scale.

Figure 9

Comparison of wall shear stress (WSS) distribution on the aortic wall for both control (left) and TGA (right) anatomies, highlighting higher WSS in anterior ascending aorta of the TGA model.