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Proceedings Paper

Development of a 3D carotid atlas for quantification of local volume change
Author(s): Xueli Chen; Yuan Zhao; J. David Spence; Bernard Chiu
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Paper Abstract

Stroke is among the leading causes of death and disability throughout the world. Carotid atherosclerosis is a focal disease predominantly occurring at bifurcations, and for this reason, local progression/regression measurements of atherosclerosis allow for more sensitive detection of treatment effect than global measurements, such as total vessel wall volume (VWV). Vessel-wall-plus-plaque thickness (VWT) change has been developed to characterize local changes and has shown to be sensitive to treatment effect, but is unable to isolate changes in individual plaque components. In this work, we propose to quantify longitudinal voxel-by-voxel plaque-and-vessel-wall volume change (ΔVVol) and represent the ΔVVol distribution on a 3D standardized atlas. Such representation allows for quantitative comparison across patients and of the measurements obtained for the same patient at different time points. We introduced a 3D non-rigid registration framework to register the carotid ultrasound images acquired at baseline and a follow-up imaging session for each patient. A 3D volume equipped with voxel-by-voxel ΔVVol was obtained by taking the divergence of the displacement field obtained in non-rigid registration. This 3D volume was uniformly sampled in the vessel wall, and the ΔVVol distribution for each patient was represented in a 3D standardized map. The proposed 3D standardized ΔVVol map allows for the characterization of feature changes on a voxel-by-voxel basis that are masked in VWT quantification. This tool has the potential to further improve the sensitivity in treatment evaluation already attained by VWT quantification.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132R (10 March 2020); doi: 10.1117/12.2549521
Show Author Affiliations
Xueli Chen, City Univ. of Hong Kong (Hong Kong, China)
Yuan Zhao, City Univ. of Hong Kong (Hong Kong, China)
J. David Spence, Robarts Research Institute (Canada)
Bernard Chiu, City Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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