ArticleJournal of medical imaging (Bellingham, Wash.)2018
Impact of automatically detected motion artifacts on coronary calcium scoring in chest computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Six months vitamin K treatment does not affect systemic arterial calcification or bone mineral density in diabetes mellitus 2.European journal of nutrition · 2021Trial
- Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.European radiology · 2025Article
- Advances in Artificial Intelligence for Non-ECG-Gated Coronary Artery Calcium Scoring: A Scoping Review.Cureus · 2025Review
- Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.European radiology · 2025Article
- Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Generative models for reproducible coronary calcium scoring.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors.BMC medical imaging · 2021Article
- Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.European radiology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The amount of coronary artery calcification (CAC) quantified in computed tomography (CT) scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one-year follow-up low-dose chest CTs from the National Lung Screening Trial. About 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Therefore, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount, the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion, where CAC quantification may not be reproducible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.