Computer methods and programs in biomedicine · 2025
Impact of aortic branch retention strategies on thrombus growth prediction in type B aortic dissection: A hemodynamic study.
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Wen Jun, Huang Qingyuan, Chen Xiaoqin, Zhang Kaiyue, Peng Liqing
Аннотация
BACKGROUND: Type B Aortic Dissection (TBAD) is a serious cardiovascular condition treated effectively by TEVAR (Thoracic Endovascular Aortic Repair), which promotes false lumen thrombosis with minimal invasiveness. However, the impact of aortic branch retention strategies on thrombus growth prediction is often underestimated.
METHOD: This study numerically investigated four branch retention strategies: preserving all branches (Type 1 strategy), removing all branches (Type 2 strategy), removing only the aortic arch branches (Type 3 strategy), and removing only the abdominal aortic branches (Type 4 strategy).
RESULTS: Type 4 strategy demonstrates similar hemodynamic stability, shear stress distribution, and thrombus formation risk as Type 1, while simplifying the anatomical structure. In contrast, complete branch removal (Type 2) and retention of only the aortic arch branches (Type 3) lead to significant flow disturbances and hemodynamic instability, potentially increasing the risk of false lumen expansion and thrombus misjudgment. Additionally, Type 4 strategy shows potential value in image simplification and deep learning applications by reducing the workload of image segmentation and 3D reconstruction while improving model training efficiency and accuracy.
CONCLUSION: This study recommends prioritizing the Type 4 strategy in aortic image simplification and TEVAR surgical planning to maintain hemodynamic stability while reducing computational complexity. This approach has significant implications for both personalized treatment and deep learning-based analyses.
METHOD: This study numerically investigated four branch retention strategies: preserving all branches (Type 1 strategy), removing all branches (Type 2 strategy), removing only the aortic arch branches (Type 3 strategy), and removing only the abdominal aortic branches (Type 4 strategy).
RESULTS: Type 4 strategy demonstrates similar hemodynamic stability, shear stress distribution, and thrombus formation risk as Type 1, while simplifying the anatomical structure. In contrast, complete branch removal (Type 2) and retention of only the aortic arch branches (Type 3) lead to significant flow disturbances and hemodynamic instability, potentially increasing the risk of false lumen expansion and thrombus misjudgment. Additionally, Type 4 strategy shows potential value in image simplification and deep learning applications by reducing the workload of image segmentation and 3D reconstruction while improving model training efficiency and accuracy.
CONCLUSION: This study recommends prioritizing the Type 4 strategy in aortic image simplification and TEVAR surgical planning to maintain hemodynamic stability while reducing computational complexity. This approach has significant implications for both personalized treatment and deep learning-based analyses.