24 February 2017 Multi-contrast MRI registration of carotid arteries based on cross-sectional images and lumen boundaries
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Abstract
Ischemic stroke has great correlation with carotid atherosclerosis and is mostly caused by vulnerable plaques. It’s particularly important to analysis the components of plaques for the detection of vulnerable plaques. Recently plaque analysis based on multi-contrast magnetic resonance imaging has attracted great attention. Though multi-contrast MR imaging has potentials in enhanced demonstration of carotid wall, its performance is hampered by the misalignment of different imaging sequences. In this study, a coarse-to-fine registration strategy based on cross-sectional images and wall boundaries is proposed to solve the problem. It includes two steps: a rigid step using the iterative closest points to register the centerlines of carotid artery extracted from multi-contrast MR images, and a non-rigid step using the thin plate spline to register the lumen boundaries of carotid artery. In the rigid step, the centerline was extracted by tracking the crosssectional images along the vessel direction calculated by Hessian matrix. In the non-rigid step, a shape context descriptor is introduced to find corresponding points of two similar boundaries. In addition, the deterministic annealing technique is used to find a globally optimized solution. The proposed strategy was evaluated by newly developed three-dimensional, fast and high resolution multi-contrast black blood MR imaging. Quantitative validation indicated that after registration, the overlap of two boundaries from different sequences is 95%, and their mean surface distance is 0.12 mm. In conclusion, the proposed algorithm has improved the accuracy of registration effectively for further component analysis of carotid plaques.
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Yu-Xia Wu, Xi Zhang, Xiao-Pan Xu, Yang Liu, Guo-Peng Zhang, Bao-Juan Li, Hui-Jun Chen, Hong-Bing Lu, "Multi-contrast MRI registration of carotid arteries based on cross-sectional images and lumen boundaries", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013323 (24 February 2017); doi: 10.1117/12.2254191; https://doi.org/10.1117/12.2254191
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