This dataset includes time-resolved 3D computed tomography angiography (4D-CTA) images of abdominal aortic aneurysms (AAA) acquired throughout the cardiac cycle from ten patients, along with ten patient-specific AAA surface models extracted from these images. The dataset was used in the study published in the Journal of Biomechanics, titled “Kinematics of Abdominal Aortic Aneurysms.” 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. The images were acquired at Fiona Stanley Hospital in Western Australia and provided to researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed. The AAA surface models 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, 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. 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 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 model. This dataset enabled the analysis of AAA wall displacement and strain throughout the cardiac cycle using a non-invasive, in vivo, image registration-based approach. The use of widely adopted open-source file formats (NRRD for images and STL for surface models) facilitates broad applicability and reusability of this dataset in AAA biomechanics studies.