Performance of a feature-based algorithm for 3D-3D registration of CT angiography to cone-beam CT for endovascular repair of complex abdominal aortic aneurysms
Journal article, 2018

Background: A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy of a new fully automatic, feature-based algorithm for 3D3D registration of CTA to CBCT. Methods: The feature-based algorithm was tested on clinical image datasets from 14 patients undergoing complex endovascular aortic repair. Deviations in Euclidian distances between vascular as well as bony landmarks were measured and compared to an intensity-based, normalized mutual information algorithm. Results: The results for the feature-based algorithm showed that the median 3D registration error between the anatomical landmarks of CBCT and CT images was less than 3mm. The feature-based algorithm showed significantly better accuracy compared to the intensity-based algorithm (p<0.001). Conclusion: A feature-based algorithm for 3D image registration is presented.

Aortic aneurysm

Cone-beam CT

Feature-based registration

Image registration

Intensity-based registration

Author

Giasemi Koutouzi

Sahlgrenska Academy

Behrooz Nasihatkton

K. N. Toosi University of Technology

Monika Danielak-Nowak

Sahlgrenska Academy

Henrik Leonhardt

Sahlgrenska Academy

Mårten Falkenberg

Sahlgrenska Academy

Fredrik Kahl

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Lund University

BMC Medical Imaging

1471-2342 (ISSN)

Vol. 18 1 42

Subject Categories

Radiology, Nuclear Medicine and Medical Imaging

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1186/s12880-018-0285-1

PubMed

30409129

More information

Latest update

1/24/2019