FASEN2023 - Wall segmentation of aorta in IVUS images

Currently, abdominal aortic aneurysms (AAAs) are treated based on the diameter of the aorta measured with ultrasound (US), however, a better patient-specific marker is needed. The mean thickness of the wall is a major indicator for AAA rupture risk, which varies significantly within and between patients. So far, regional thickness has not been used in previous rupture risk analysis studies, since it cannot be measured with CT, MRI, nor with non-invasive US. This is the first study to map locally varying wall thickness of AAAs using intravascular ultrasound (IVUS).Since no ground truth of AAA wall thickness can be obtained in vivo, a novel ex vivo dataset was created of porcine, phantom and simulated aortas, including a ground truth. The porcine aortas introduce the image features found in real aortic tissue, the phantom aortas introduce the motion by pulsation of the aorta, and the simulated aortas introduce realistic aneurysm geometries. The ground truth wall geometry for the different experimental set-ups were obtained in various ways, i.e. for porcine aortas, micro-CT was used, for phantom aortas, manual segmentation and a known wall thickness were used, and for simulated AAAs, a given wall geometry was used as input.This ex vivo dataset can be used to train a neural network, and a trained model can successfully segment the aortic wall in IVUS images in AAAs in vivo. Regionally varying wall thickness and geometry of aortas can be obtained, leading to a patient-specific marker for more advanced rupture risk assessment of AAAs.