Cardiac Imaging and Diagnostics
Abstract
Abstract
Background: Coronary artery calcium (CAC) is an established measure of coronary atherosclerosis from computed tomography (CT). While deep learning (DL) can quantify CAC from non-dedicated CT, the accuracy is limited by image quality. Purpose: We derived and validated a novel method for DL CAC segmentation on ultra-low dose CT attenuation correction (CTAC) scans that is trained with synthetic low-dose, ungated images. Materials and Methods: Models were trained using one center and externally tested in two other centers. Synthetic, ungated CT scans were generated so that expert segmentations from dedicated CAC scans could be used as ground truth for perfectly registered synthetic images through knowledge adaptation (KAD-CAC). We evaluated agreement between CAC scoring methods vs expert readers on a per-patient and per-vessel basis, as well as associations with the primary outcome of death or myocardial infarction (MI). Results: The DL models were externally tested on 5969 patients with a median age of 64 (IQR 56 - 73), of whom 50.2% were male. The KAD-CAC model had higher Cohens kappa K (0.86, 95% CI 0.85 - 0.87) compared to previous convolutional LSTM model (K 0.78, 95% CI 0.76 - 0.80, p<0.01), or models trained with only gated images (K 0.81, 95% CI 0.80 - 0.82, p<0.01). Net reclassification improvement for CAC stratified risk of death or MI, was greatest for the KAD-CAC model over baseline including age, sex, hypertension, diabetes, dyslipidemia, family history, smoking, stress total perfusion deficit, and left ventricular ejection fraction. Conclusion: We use paired synthetic ungated scans to transfer expert gated CAC annotations into the ungated domain, resulting in substantially better vessel-level CAC scoring and improved risk stratification.
Direct answer
What can I do from this paper page?
Use this page to scan "Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Cardiac Imaging and Diagnostics research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Shanbhag2026Cross,
title = {Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans},
author = {Aakash Shanbhag and R O B E R T Miller and Aditya Killekar and Anna M Marcinkiewicz and Jianhang Zhou and Mark Lemley and Assiata Kamagate and Serge D. Van Kriekinge and Paul B. Kavanagh and Attila Feher and Edward J. Miller and Jiaming Liang and Daniel S Berman and D A M I N I Dey and Richard M. Leahy and Piotr J. Slomka},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.07.02.26356002},
url = {https://doi.org/10.64898/2026.07.02.26356002}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Cardiac Imaging and Diagnostics research papers?
Follow Cardiac Imaging and Diagnostics research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app