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A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography

Mahmut S. Gokmen, Moneera N. Haque, Steve W. Leung, Caroline N. Leach, Seth Parker, Stephen B. Hobbs, Vincent L. Sorrell, W. Brent Seales, V. K. Cody Bumgardner

TL;DR

An automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans is introduced, demonstrating the feasibility of cross-domain CAC scoring from gated to non-gated domains.

Abstract

Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and a Cohen's kappa of 0.528 on the Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's kappa scores of 0.871 and 0.874 across independent datasets, demonstrating robust risk stratification. These results demonstrate the feasibility of cross-domain CAC scoring from gated to non-gated domains, supporting scalable cardiovascular screening in routine chest imaging without additional scans or annotations.

A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography

TL;DR

An automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans is introduced, demonstrating the feasibility of cross-domain CAC scoring from gated to non-gated domains.

Abstract

Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and a Cohen's kappa of 0.528 on the Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's kappa scores of 0.871 and 0.874 across independent datasets, demonstrating robust risk stratification. These results demonstrate the feasibility of cross-domain CAC scoring from gated to non-gated domains, supporting scalable cardiovascular screening in routine chest imaging without additional scans or annotations.
Paper Structure (5 sections, 5 figures, 6 tables)

This paper contains 5 sections, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Results
  4. Discussion and Conclusion
  5. Acknowledgments

Figures (5)

  • Figure 1: Overview of the automated CAC scoring architecture for coronary artery calcium detection and quantification.
  • Figure 2: Attention maps from CARD-ViT highlighting calcified regions in non-gated (a) and gated (b) CT scans. Each pair shows a raw CT slice (left) and the corresponding PCA-transformed attention visualization (right) from the self-supervised ViT backbone.
  • Figure 3: CARD-ViT segmentation pipeline: ViT feature extraction, $784$-channel feature fusion, decoder upsampling with selective skip connections, and final binary mask output.
  • Figure 4: Automated segmentation by CARD-ViT (red overlay) visualized in the OHIF Viewer via MONAI Label for real-time clinical review.
  • Figure 5: Qualitative segmentation comparison on gated CT slices. Left: Ground-truth annotation overlay. Middle: CARD-ViT prediction showing accurate calcification localization. Right: AI-CAC prediction (trained on non-gated data) demonstrating domain mismatch with over-segmentation artifacts.