Literature DB >> 18777929

Quantification of carotid vessel wall and plaque thickness change using 3D ultrasound images.

Bernard Chiu1, Micaela Egger, J David Spence, Grace Parraga, Aaron Fenster.   

Abstract

Quantitative measurements of carotid plaque burden progression or regression are important in monitoring patients and in evaluation of new treatment options. 3D ultrasound (US) has been used to monitor the progression or regression of carotid artery plaques. This paper reports on the development and application of a method used to analyze changes in carotid plaque morphology from 3D US. The technique used is evaluated using manual segmentations of the arterial wall and lumen from 3D US images acquired in two imaging sessions. To reduce the effect of segmentation variability, segmentation was performed five times each for the wall and lumen. The mean wall and lumen surfaces, computed from this set of five segmentations, were matched on a point-by-point basis, and the distance between each pair of corresponding points served as an estimate of the combined thickness of the plaque, intima, and media (vessel-wall-plus-plaque thickness or VWT). The VWT maps associated with the first and the second US images were compared and the differences of VWT were obtained at each vertex. The 3D VWT and VWT-Change maps may provide important information for evaluating the location of plaque progression in relation to the localized disturbances of flow pattern, such as oscillatory shear, and regression in response to medical treatments.

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Year:  2008        PMID: 18777929     DOI: 10.1118/1.2955550

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  8 in total

1.  Fast plaque burden assessment of the femoral artery using 3D black-blood MRI and automated segmentation.

Authors:  Bernard Chiu; Jie Sun; Xihai Zhao; Jinnan Wang; Niranjan Balu; Jiachang Chi; Jianrong Xu; Chun Yuan; William S Kerwin
Journal:  Med Phys       Date:  2011-10       Impact factor: 4.071

2.  Conformal mapping of carotid vessel wall and plaque thickness measured from 3D ultrasound images.

Authors:  Gary P T Choi; Yimin Chen; Lok Ming Lui; Bernard Chiu
Journal:  Med Biol Eng Comput       Date:  2017-06-07       Impact factor: 2.602

3.  Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images.

Authors:  Wei Ma; Yujiao Xia; Xiaoyan Wu; Zheng Yue; Xinyao Cheng; Aaron Fenster; Mingyue Ding
Journal:  Comput Math Methods Med       Date:  2022-04-25       Impact factor: 2.809

4.  Three-dimensional ultrasound measurements of carotid vessel wall and plaque thickness and their relationship with pulmonary abnormalities in ex-smokers without airflow limitation.

Authors:  Jieyu Cheng; Damien Pike; Tommy W S Chow; Miranda Kirby; Grace Parraga; Bernard Chiu
Journal:  Int J Cardiovasc Imaging       Date:  2016-06-24       Impact factor: 2.357

5.  Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images.

Authors:  Ran Zhou; Aaron Fenster; Yujiao Xia; J David Spence; Mingyue Ding
Journal:  Med Phys       Date:  2019-06-11       Impact factor: 4.071

6.  Quantitative assessment of carotid artery atherosclerosis by three-dimensional magnetic resonance and two-dimensional ultrasound imaging: a comparison study.

Authors:  Huiyu Qiao; Ying Cai; Manwei Huang; Yang Liu; Qiang Zhang; Lingyun Huang; Huijun Chen; Chun Yuan; Xihai Zhao
Journal:  Quant Imaging Med Surg       Date:  2020-05

7.  Ultrasound common carotid artery segmentation based on active shape model.

Authors:  Xin Yang; Jiaoying Jin; Mengling Xu; Huihui Wu; Wanji He; Ming Yuchi; Mingyue Ding
Journal:  Comput Math Methods Med       Date:  2013-03-06       Impact factor: 2.238

8.  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

  8 in total

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