Literature DB >> 18815090

A "twisting and bending" model-based nonrigid image registration technique for 3-D ultrasound carotid images.

Nuwan D Nanayakkara1, Bernard Chiu, Abbas Samani, J David Spence, Jagath Samarabandu, Aaron Fenster.   

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. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a "twisting and bending" model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. 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 after applying our "twisting and bending" model-based nonrigid registration algorithm. We achieved an average registration error of 0.80 +/-0.26 mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.

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Year:  2008        PMID: 18815090     DOI: 10.1109/TMI.2008.918326

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  2 in total

1.  A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features.

Authors:  Anupama Gupta; Harsh K Verma; Savita Gupta
Journal:  Med Biol Eng Comput       Date:  2013-05-25       Impact factor: 2.602

2.  A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration.

Authors:  Hengkai Guo; Guijin Wang; Lingyun Huang; Yuxin Hu; Chun Yuan; Rui Li; Xihai Zhao
Journal:  PLoS One       Date:  2016-02-16       Impact factor: 3.240

  2 in total

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