Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents

TL;DR

This paper introduces an efficient segmentation algorithm for thoracic and abdominal aortic aneurysms in CTA images, enabling accurate size measurement, 3D reconstruction, and endoleak detection for improved aneurysm monitoring.

Contribution

The novel algorithm combines grey level thresholding and active contour models with constraints and an opacity-guided deformation to accurately segment aneurysm boundaries and detect endoleaks.

Findings

Successfully segmented aneurysms in nine clinical CTA datasets.

Enabled precise measurement and 3D modeling of aneurysms.

Demonstrated promising results in endoleak detection.

Abstract

In this paper we present an efficient algorithm for the segmentation of the inner and outer boundary of thoratic and abdominal aortic aneurysms (TAA & AAA) in computed tomography angiography (CTA) acquisitions. The aneurysm segmentation includes two steps: first, the inner boundary is segmented based on a grey level model with two thresholds; then, an adapted active contour model approach is applied to the more complicated outer boundary segmentation, with its initialization based on the available inner boundary segmentation. An opacity image, which aims at enhancing important features while reducing spurious structures, is calculated from the CTA images and employed to guide the deformation of the model. In addition, the active contour model is extended by a constraint force that prevents intersections of the inner and outer boundary and keeps the outer boundary at a distance, given by the thrombus thickness, to the inner boundary. Based upon the segmentation results, we can measure the aneurysm size at each centerline point on the centerline orthogonal multiplanar reformatting (MPR) plane. Furthermore, a 3D TAA or AAA model is reconstructed from the set of segmented contours, and the presence of endoleaks is detected and highlighted. The implemented method has been evaluated on nine clinical CTA data sets with variations in anatomy and location of the pathology and has shown promising results.