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Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT.
- Source :
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Medical engineering & physics [Med Eng Phys] 2024 Jul; Vol. 129, pp. 104182. Date of Electronic Publication: 2024 May 22. - Publication Year :
- 2024
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Abstract
- Background: The high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments.<br />Methods: Non-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model.<br />Results: The experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only.<br />Conclusion: The overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.<br />Competing Interests: Declaration of Competing Interest None declared.<br /> (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-4030
- Volume :
- 129
- Database :
- MEDLINE
- Journal :
- Medical engineering & physics
- Publication Type :
- Academic Journal
- Accession number :
- 38906576
- Full Text :
- https://doi.org/10.1016/j.medengphy.2024.104182