title: A non-rigid image registration technique for 3D ultrasound carotid images using a "twisting and bending" model.
abstract: Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Sensitive monitoring of plaque changes in volume and morphology requires that 3D ultrasound (US) images of carotid plaque obtained at different time points be registered and evaluated for change. This registration technique should be non-rigid, since different head positions in image acquisitions cause relative bending and torsion in the neck, producing non-linear deformations between the images. We modeled the movement of the neck using a "twisting and bending model" with only six parameters for non-rigid registration. We used a Mutual Information (MI) based image similarity measure with the Powell optimization method as they have been used effectively in US image registration applications. For evaluation of our algorithm, we acquired 3D US carotid images from three subjects at two different head positions to simulate images acquired at different times. Then, we registered each image set using our "twisting bending model" based non-rigid registration algorithm. We calculated the Mean Registration Error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric. We repeated the experiment with only rigid registration to compare the capabilities of the proposed algorithm in improving registration of 3D carotid US images. The average registration error was 1.03+/-0.23 mm using our non-rigid registration technique, while it was 1.50+/-0.50 mm when we applied the rigid registration alone.
authors.affiliation: Imaging Res. Lab., Univ. of Western Ontario, London, Ont., Canada. nnanayak@uwo.ca
authors.forename: N D
authors.initials: ND
authors.lastname: Nanayakkara
authors.forename: B
authors.initials: B
authors.lastname: Chiu
authors.forename: A
authors.initials: A
authors.lastname: Samani
authors.forename: J D
authors.initials: JD
authors.lastname: Spence
authors.forename: J
authors.initials: J
authors.lastname: Samarabandu
authors.forename: A
authors.initials: A
authors.lastname: Fenster
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique