CoronaryFusion-Net++: A calcium-aware transformer fusion network for coronary calcium removal in cardiac CT angiography

Abstract

Background

Coronary artery calcium (CAC) causes blooming artifacts in coronary computed tomography angiography (CCTA), leading to overestimation of luminal stenosis and reduced diagnostic reliability in heavily calcified coronary arteries. Existing convolutional neural networks have limited receptive fields, whereas transformer-based models often incur high computational complexity and inadequate local feature preservation.

Objective

This study proposes CoronaryFusion-Net++, a calcium-aware hybrid deep learning framework for suppressing coronary calcium blooming artifacts while preserving coronary lumen morphology and anatomical continuity.

Methods

CoronaryFusion-Net++ integrates a Dense Encoder Module, a Cross-Scale Fusion Module (CSFM), a Calcium-Aware Swin Transformer Bottleneck, and an Attention-Guided Decoder with Adaptive Multi-Scale Upsampling. The network was trained and evaluated on the AIMI-COCA dataset using patient-level five-fold cross-validation. Performance was assessed using PSNR, SSIM, Dice coefficient, IoU, Precision, Recall, and computational complexity. Ablation experiments quantified the contribution of each architectural component.

Results

CoronaryFusion-Net++ achieved a Dice coefficient of 98.83%, an IoU of 97.69%, an SSIM of 0.986, and a PSNR of 39.84 dB, outperforming representative convolutional and transformer-based baseline methods. Furthermore, the framework demonstrated superior anatomical reconstruction and blooming artifact reduction, yielding a localized Lumen Boundary Precision (LBP) of 0.21 mm and an Apparent Stenosis Error (ASE) reduction of 18.42%. Ablation studies demonstrated consistent performance improvements following the integration of dense feature extraction, cross-scale feature fusion, transformer-based contextual modelling, and attention-guided reconstruction while maintaining computational efficiency.

Conclusion

CoronaryFusion-Net++ provides an effective framework for calcium blooming suppression and coronary lumen preservation in CCTA images. By combining local feature representation with global contextual modelling, the proposed architecture improves image reconstruction quality while maintaining computational feasibility. These findings indicate its potential to support more reliable stenosis assessment in heavily calcified coronary arteries and provide a foundation for future clinical validation and deployment.