Automatic coronary artery plaque thickness comparison between baseline and follow‐up CCTA images. Issue 3 (20th January 2020)

• Record Type: Journal Article
• Authors: Cao, Qing; Broersen, Alexander; Kitslaar, Pieter H.; Yuan, Mingyuan; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke
• Is Part Of: Medical physics. Volume 47:Issue 3(2020)
• Page Start: 1083
• Page End: 1093
• Publication Date: 2020-01-20
• Subjects: baseline and follow‐up -- coronary arteries -- coronary computed tomography angiography -- plaque thickness -- visualization; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13993
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Currently, 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. Methods: We 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. Results: For 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). Conclusions: The 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.