Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets

Xinpei Gao1, Pieter H Kitslaar2,3, Ricardo P J Budde4

  • 1Division of Image Processing, Department of Radiology, Leiden University Medical Center, 9600, 2300 RC, Leiden, The Netherlands.

Insights

A new automated method accurately extracts the aorto-femoral artery trajectory from CT angiography (CTA) scans. This technique supports pre-procedural planning for trans-catheter aortic valve replacement (TAVR).

Area of Science:

  • Medical Imaging
  • Cardiovascular Interventions
  • Computational Anatomy

Background:

  • Accurate segmentation of the aorto-femoral artery is crucial for planning trans-catheter aortic valve replacement (TAVR).
  • Current methods often require manual or semi-automated segmentation, which can be time-consuming and subject to inter-observer variability.
  • Developing an automated approach can streamline the workflow and improve consistency in pre-procedural assessments.

Purpose of the Study:

  • To develop and validate a fully automated technique for extracting the entire arterial access route from the femoral artery to the aortic root using computed tomography angiography (CTA) data.
  • To compare the accuracy and reliability of the automated method against semi-automated results from experienced observers.

Main Methods:

  • An automatic vessel tracking algorithm was employed to identify the centerline connecting femoral access points to the aortic root.
  • A deformable 3D-model fitting method was utilized to delineate the lumen boundary of the vascular trajectory.
  • The framework was validated on whole-body CTA datasets from 36 patients, comparing automated segmentations with those from two observers.

Main Results:

  • The automated method achieved high Dice similarity indexes (0.950-0.982) compared to observers, demonstrating strong agreement.
  • Inter-observer variability was comparable to the agreement between the automated method and observers.
  • The automated method showed a slight, statistically significant overestimation of minimal luminal diameter (MLD) in the ilio-femoral artery and aorta compared to manual measurements.

Conclusions:

  • The proposed fully automated segmentation approach provides reliable measurements of the aorto-femoral arterial access route.
  • This technique has the potential to significantly support and enhance the image-guided work-up for TAVR procedures.
  • This represents the first fully automatic segmentation method for the entire aorto-femoral vessel trajectory in CTA images.