Generative Models for Reproducible Coronary Calcium Scoring

• URL: http://arxiv.org/abs/2205.11967v1
• Date: Tue, 24 May 2022 10:59:32 GMT
• Title: Generative Models for Reproducible Coronary Calcium Scoring
• Authors: Sanne G.M. van Velzen, Bob D. de Vos, Julia M.H. Noothout, Helena M. Verkooijen, Max A. Viergever, Ivana I\v{s}gum
• Abstract summary: Coronary artery calcium (CAC) score is a strong and independent predictor of coronary heart disease (CHD) events. CAC scoring suffers from limited interscan due to clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. We propose a CAC method that does not require a threshold for segmentation of CAC.
• Score: 3.1746159467221253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Coronary artery calcium (CAC) score, i.e. the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-ECG-synchronized CT where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a threshold for segmentation of CAC. Approach: Our method utilizes a generative adversarial network where a CT with CAC is decomposed into an image without CAC and an image showing only CAC. The method, using a CycleGAN, was trained using 626 low-dose chest CTs and 514 radiotherapy treatment planning CTs. Interscan reproducibility was compared to clinical calcium scoring in radiotherapy treatment planning CTs of 1,662 patients, each having two scans. Results: A lower relative interscan difference in CAC mass was achieved by the proposed method: 47% compared to 89% manual clinical calcium scoring. The intraclass correlation coefficient of Agatston scores was 0.96 for the proposed method compared to 0.91 for automatic clinical calcium scoring. Conclusions: The increased interscan reproducibility achieved by our method may lead to increased reliability of CHD risk categorization and improved accuracy of CHD event prediction.

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