Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients

Abstract

Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch. Hemodynamic characteristics were estimated in key subdomains of the aorta and three branches using computational fluid dynamics methods. The key morphometric features (diameters) were calculated at locations in proximity to the domains, where hemodynamic characteristics are evaluated. Results: The functional dependencies for velocities and pressure on the corresponding diameters have been fitted, and a metamodel has been constructed employing the predicted values from these models. Conclusions: The metamodel demonstrated high accuracy in classifying aortas into their respective types, thereby confirming the adequacy of the predicted hemodynamic characteristics by morphometric characteristics. The proposed methodology is applicable to other heart diseases without fundamental changes.

Keywords: aorta; biomechanical features; classification; infant cardiovascular surgery; metamodel; patient-oriented modeling.

Figure 1

An example of an aorta with markers illustrating positions for measuring morphometric features (diameters) and spheres for evaluating hemodynamic features.

Figure 2

Correlation coefficient $r(D_{\mathrm{auto}},D\mathrm{manual})$ for manually and automatically evaluated diameters.

Figure 3

Scatter plots demonstrating pairwise relationships between cross-section diameters $D_i,i=\overline{1,5}$, $D_{\mathrm{BCA}}$, $D_{\mathrm{LCCA}}$, $D_{\mathrm{LSCA}}$ and $D_{\mathrm{CoA}}$, in mm.

Figure 4

Velocity streamlines in regular aortas from the prepared dataset.

Figure 5

Pressure distribution in regular aortas from the prepared dataset.

Figure 6

Velocity streamlines in aortas with CoA diagnosis from the prepared dataset.

Figure 7

Pressure distribution in aortas with CoA diagnosis from the prepared dataset.

Figure 8

Scatter plots demonstrating pairwise relationships between averaged velocities calculated in critical domains $P_1$, $O_{\mathrm{BCA}}$, $O_{\mathrm{LSCA}}$, $O_{\mathrm{LSCA}}$, $M_2$, $M_3$, $M_4$ and $O_5$.

Figure 9

Scatter plots demonstrating pairwise relationships between averaged pressure calculated in critical domains $P_1$, $O_{\mathrm{BCA}}$, $O_{\mathrm{LSCA}}$, $O_{\mathrm{LSCA}}$, $M_2$, $M_3$, $M_4$ and $O_5$.

Figure 10

Mean of the estimated diameters.

Figure 11

Mean values of the calculated hemodynamic characteristics at the points.

Figure 12

Correlation coefficient $r(H,\widehat{H})$ for manually and automatically evaluated diameters.