4D-CTA image and geometry dataset for kinematic analysis of abdominal aortic aneurysmsZenodo
This article presents a dataset used in the article “Kinematics of Abdominal Aortic Aneurysms” [1], published in the Journal of Biomechanics. The dataset is publicly available for download from the Zenodo data repository (10.5281/zenodo.15477710). The dataset includes time-resolved 3D computed tomog...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925005244 |
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| Summary: | This article presents a dataset used in the article “Kinematics of Abdominal Aortic Aneurysms” [1], published in the Journal of Biomechanics. The dataset is publicly available for download from the Zenodo data repository (10.5281/zenodo.15477710). The dataset includes time-resolved 3D computed tomography angiography (4D-CTA) images of abdominal aortic aneurysm (AAA) captured throughout the cardiac cycle from ten patients diagnosed with AAA, along with ten patient-specific AAA geometries extracted from these images. Typically, the 4D-CTA dataset for each patient contains ten electrocardiogram (ECG)-gated 3D-CTA image frames acquired over a cardiac cycle, capturing both the systolic and diastolic phases of the AAA configuration. For method verification, the dataset also includes synthetic ground truth data generated from Patient 1’s 3D-CTA AAA image in the diastolic phase. The ground truth data includes the patient-specific finite element (FE) biomechanical model and a synthetic systolic 3D-CTA image. The synthetic systolic image was generated by warping Patient 1’s diastolic 3D-CTA image using the realistic displacement field obtained from the AAA biomechanical FE model. The images were acquired at Fiona Stanley Hospital in Western Australia and provided to the researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed using a newly created algorithm, as described in [1]. The AAA geometries were extracted using an automated image processing pipeline comprising AI-based segmentation with PRAEVAorta software by NUREA (https://www.nurea-soft.com/), automated post-processing with the ISML-UWA in-house code (https://arxiv.org/abs/2403.07238), and surface model extraction using the freely available BioPARR (Biomechanics-based Prediction of Aneurysm Rupture Risk) (https://bioparr.mech.uwa.edu.au/) and 3D Slicer (https://www.slicer.org/) software packages [2,3]. Our dataset enabled the analysis of AAA wall displacement and strain throughout the cardiac cycle using a non-invasive, in vivo, image registration-based approach [1]. The use of widely adopted, open-source file formats—NRRD for images and STL for geometries—facilitates broad applicability and reusability in AAA biomechanics studies that require patient-specific geometry and information about AAA kinematics during cardiac cycle. |
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| ISSN: | 2352-3409 |