Impact of aortic branch retention strategies on thrombus growth prediction in type B aortic dissection: A hemodynamic study

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-02-22 DOI:10.1016/j.cmpb.2025.108679

Jun Wen , Qingyuan Huang , Xiaoqin Chen , Kaiyue Zhang , Liqing Peng

{"title":"Impact of aortic branch retention strategies on thrombus growth prediction in type B aortic dissection: A hemodynamic study","authors":"Jun Wen , Qingyuan Huang , Xiaoqin Chen , Kaiyue Zhang , Liqing Peng","doi":"10.1016/j.cmpb.2025.108679","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Method</h3><div>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).</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"263 ","pages":"Article 108679"},"PeriodicalIF":4.9000,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260725000963","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.