ProDM: AI for Motion Artifact Correction in Chest CT
Published:Dec 31, 2025 16:29
•1 min read
•ArXiv
Analysis
This paper presents a novel AI framework, ProDM, to address the problem of motion artifacts in non-gated chest CT scans, specifically for coronary artery calcium (CAC) scoring. The significance lies in its potential to improve the accuracy of CAC quantification, which is crucial for cardiovascular disease risk assessment, using readily available non-gated CT scans. The use of a synthetic data engine for training, a property-aware learning strategy, and a progressive correction scheme are key innovations. This could lead to more accessible and reliable CAC scoring, improving patient care and potentially reducing the need for more expensive and complex ECG-gated CT scans.
Key Takeaways
- •ProDM is a generative diffusion model designed to correct motion artifacts in non-gated chest CT scans.
- •It uses a synthetic data engine, property-aware learning, and a progressive correction scheme.
- •The model improves CAC scoring accuracy, lesion fidelity, and risk stratification.
- •It has the potential to make CAC scoring more accessible and reliable.
Reference
“ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines.”