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

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.

Is There a Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?

BACKGROUND: Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is typically an asymptomatic condition that if left untreated can expand to the point of rupture. In simple mechanical terms, rupture of an artery occurs when the local wall stress exceeds the local wall strength. It is therefore understandable that numerous studies have attempted to estimate the AAA wall stress and investigate the relationship between the AAA wall stress and AAA symptoms. MATERIALS AND METHODS: We conducted computational biomechanics analysis for 19 patients with AAA (a proportion of these patients were classified as symptomatic) to investigate whether the AAA wall stress fields (both the patterns and magnitude) correlate with the clinical definition of symptomatic and asymptomatic AAAs. For computation of AAA wall stress, we used a very efficient method recently presented by the Intelligent Systems for Medicine Laboratory. The Intelligent Systems for Medicine Laboratory's method uses geometry from computed tomography images and mean arterial pressure as the applied load. The method is embedded in the software platform BioPARR-Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from http://bioparr.mech.uwa.edu.au/. The uniqueness of our stress computation approach is three-fold: i) the results are insensitive to unknown patient-specific mechanical properties of arterial wall tissue; ii) the residual stress is accounted for, according to Y.C. Fung's Uniform Stress Hypothesis; and iii) the analysis is automated and quick, making our approach compatible with clinical workflows. RESULTS: Symptomatic patients could not be identified from the plots (pattern) of AAA wall stress and stress magnitude. Although the largest stress was predicted for a patient who suffered from AAA symptoms, the three patients with the smallest stress were also symptomatic. CONCLUSIONS: The results demonstrate, contrary to the common view, that neither the wall stress magnitude nor the stress distribution appears to be associated with the presence of clinical symptoms.