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

• Published in: BMC medical imaging Vol. 18; no. 1; pp. 42 - 8
• Main Authors: Koutouzi, Giasemi, Nasihatkton, Behrooz, Danielak-Nowak, Monika, Leonhardt, Henrik, Falkenberg, Mårten, Kahl, Fredrik
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.11.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Abdomen; Abdominal aortic aneurysm; Abdominal viscera and gastrointestinal tract imaging; Accuracy; Algorithms; Aneurysms; Angiography; Aortic aneurysm; Aortic aneurysms; Cardiovascular system; Care and treatment; CAT scans; Clinical Medicine; Computed tomography; Computer vision; Cone-beam CT; Coronary vessels; Feature-based registration; Image processing; Image registration; Imaging; Intensity-based registration; Klinisk medicin; Liver; Medical and Health Sciences; Medical imaging; Medicin och hälsovetenskap; Medicine; Medicine & Public Health; Radiologi och bildbehandling; Radiology; Radiology and Medical Imaging; Registration; Technical Advance; Vascular surgery; Workflow; Cone-beam CT; Image registration; Intensity-based registration; Feature-based registration; Aortic aneurysm
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-018-0285-1

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 3 mm. 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.