Development of an automatic modeling method for patient-specific aortic graft reconstruction guide in thoracoabdominal aortic repair

Abstract

Background and objectives: Because repairing visceral and segmental arteries in open surgical repair for thoracoabdominal aortic aneurysms is essential, two types of patient-specific graft reconstruction guides for reconstruction in the operating room have been developed that are applied clinically. However, designing the patient-specific guides is a time-consuming, laborious task. The aim of this study was to develop an automatic modeling method and to evaluate its accuracy.

Methods: In 10 patients with thoracoabdominal aortic aneurysms, computer-aided designing was performed with conventional and automatic modeling methods for aortic reconstruction guides as follows: 1) a visualizing guide that presented the accurate shape of the aortic graft, visualizing the main aortic body and major blood vessels; and 2) a marking guide wherein the vessels in the visualizing guide were replaced by the protruding marking regions detectable by tactile sense. The script-based automatic guide modeling program was developed using an application programming interface presented in the 3-matic software with Python. For accuracy, the absolute mean differences of both modeling methods were assessed using Hausdorff distance. The modeling between conventional and automatic modeling methods was compared and evaluated using the Wilcoxon signed-rank test.

Results: The absolute mean difference between the conventional and automatic modeling methods were 6.05 ± 4.86 µm for the visualizing guide and 5.51 ± 4.85 µm for the marking guide. For the visualizing guide, the modeling time of the automatic modeling method was reduced by approximately more than thirtyfold than the conventional modeling method (p<0.001). The marking guide was reduced about fortyfold (p<0.001).

Conclusions: Compared to the conventional method, the automatic modeling method was demonstrated to reduce the modeling time with reasonable accuracy, which could lead to a more efficient modeling and clinical application.