Image, geometry and finite element mesh datasets for analysis of relationship between abdominal aortic aneurysm symptoms and stress in walls of abdominal aortic aneurysm

Data in Brief

Hozan Mufty¹  Angus Tavner²  Bradley Saunders³  Adam Wittek³  Christopher Rogers³  Karol Miller³  Inge Fourneau³  Ross Sciarrone³  Grand Roman Joldes³  Bart Meuris⁴  Alastair Catlin⁴ 

[1]Corresponding author.

[2]Cardiac Surgery, University Hospitals Leuven, Belgium

[3]Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia

[4]Vascular Surgery, University Hospitals Leuven, Belgium

关键词: Abdominal aortic aneurysm;    Patient-specific modelling;    Finite element method;    Symptoms;    Biomechanics;   

DOI  :  

来源: DOAJ

【 摘 要 】

These datasets contain Computed Tomography (CT) images of 19 patients with Abdominal Aortic Aneurysm (AAA) together with 19 patient-specific geometry data and computational grids (finite element meshes) created from these images applied in the research reported in Journal of Surgical Research article “Is There A Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?”[1].The images were randomly selected from the retrospective database of University Hospitals Leuven (Leuven, Belgium) and provided to The University of Western Australia's Intelligent Systems for Medicine Laboratory. The analysis was conducted using our freely-available open-source software BioPARR (Joldes et al., 2017) created at The University of Western Australia. The analysis steps include image segmentation to obtain the patient-specific AAA geometry, construction of computational grids (finite element meshes), and AAA stress computation. We use well-established and widely used data file formats (Nearly Raw Raster Data or NRRD for the images, Stereolitography or STL format for geometry, and Abaqus finite element code keyword format for the finite element meshes). This facilitates re-use of our datasets in practically unlimited range of studies that rely on medical image analysis and computational biomechanics to investigate and formulate indicators and predictors of AAA symptoms.

【 授权许可】

Unknown