ArticleMedical physics2020
Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds.Cardiology and therapy · 2024Article
- Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images.Biomedical engineering letters · 2023Article
- Utilizing (serial) coronary computed tomography angiography (CCTA) to predict plaque progression and major adverse cardiac events (MACE): results, merits and challenges.European radiology · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeCurrently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time.
methodsWe model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time.
resultsFor 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 ± 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation ± 0.1 mm).
conclusionsThe proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison.
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