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Proceedings Paper

Multicontrast MRI registration of carotid arteries in atherosclerotic and normal subjects
Author(s): Luca Biasiolli; J. Alison Noble; Matthew D. Robson
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Paper Abstract

Clinical studies on atherosclerosis agree that multi-contrast MRI is the most promising technique for in-vivo characterization of carotid plaques. Multi-contrast image registration is essential for this application, because it corrects misalignments caused by patient motion during MRI acquisition. To date, it has not been determined which automatic method provides the best registration accuracy in carotid MRI. This study tries to answer this question by presenting an iterative coarse-to-fine algorithm that co-registers multi-contrast images of carotid arteries using three similarity metrics: Correlation Ratio (CR), Mutual Information (MI) and Gradient MI (GMI). The registration algorithm is first applied on the entire images and then only on the Region of Interest (ROI) of the carotid arteries using sub-pixel accuracy. The ROI is defined by an automatic carotid detection algorithm, which was tested on a group of 20 patients with different types of atherosclerotic plaques (sensitivity 91% and specificity 88%). Automatic registration was compared with image alignment obtained by manual operators (clinically qualified vascular specialists). Registration accuracies were measured using a novel MRI validation procedure, in which the gold standard is represented by in-plane rigid transformations applied by the MRI system to mimic neck movements. Overall, automatic methods (GMI = 181 ± 104 μm) produced lower registration errors than manual operators (365 ± 102 μm). GMI performed slightly better than CR and MI, suggesting that anatomical information improves registration accuracy in the carotid ROI.

Paper Details

Date Published: 12 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76232N (12 March 2010); doi: 10.1117/12.844510
Show Author Affiliations
Luca Biasiolli, Univ. of Oxford (United Kingdom)
Oxford Ctr. for Clinical Magnetic Resonance Research (United Kingdom)
J. Alison Noble, Univ. of Oxford (United Kingdom)
Matthew D. Robson, Univ. of Oxford (United Kingdom)
Oxford Ctr. for Clinical Magnetic Resonance Research (United Kingdom)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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