High-Res Coronary CT Angiography Data

• High-resolution coronary CT angiography data are advanced volumetric imaging datasets providing sub-millimeter resolution and comprehensive visualization of coronary arteries.
• They enable precise quantitative morphology analysis, automated segmentation, and 3D reconstruction for improved risk assessment and computational simulations.
• Recent advances including photon-counting CT and deep learning techniques enhance plaque characterization and diagnostic accuracy in cardiovascular applications.

High-resolution coronary computed tomography angiography (CTA or CCTA) data refer to volumetric imaging datasets obtained using multi-detector CT systems designed for exquisite spatial visualization of the coronary arteries, their sub-millimeter branches, vessel walls, and associated pathology such as plaques and stenoses. These datasets are the foundation for quantitative morphological analysis, risk assessment, computational simulation, and image-based clinical decision support in cardiovascular medicine.

1. Imaging Principles and Data Characteristics

Contemporary CCTA datasets are acquired using multi-detector helical CT systems (commonly, 64 slices or more, including dual-source 128-slice or higher) with isotropic or near-isotropic spatial sampling and sub-millimeter in-plane resolutions (typically 0.29–0.43 mm, slice thickness 0.25–0.7 mm) (Zeng et al., 2022, Hansen et al., 7 Oct 2025). Retrospective ECG gating and iodinated contrast injection enable robust delineation of the coronary lumen, wall, and surrounding cardiac anatomy. Datasets are saved as DICOM series that are usually reconstructed into 512×512 axial images spanning 200–300 slices per acquisition.

Recent technological advancements include the use of photon-counting CT (PCCT), with deep-silicon detectors providing further spatial and spectral fidelity, as well as dynamic cardiac CT perfusion protocols that capture temporal changes in tissue attenuation (Li et al., 2023, Wu et al., 2023). These advances enable discrimination of plaque composition, assessment of motion artifacts, and improved quantitative tissue characterization.

2. Datasets and Benchmark Resources

The emergence of large annotated datasets has critically advanced algorithmic development and quantitative research in CCTA:

Dataset Name	#Cases/Scans	Annotations	Notable Features
ImageCAS	1,000	Left/right CA segmentations by ≥2 experts	Rigorous cross-checked labels, high res. (Zeng et al., 2022)
Coronary Atlas/ASOCA	40 train + 20 test	Voxel-wise arteries/centrelines/meshes	3-expert consensus, normal and diseased, STL/VTK output (Gharleghi et al., 2022)
Copenhagen GP Study / (Hansen et al., 7 Oct 2025)	980 private, 1,000 ImageCAS public	High-resolution LAA, PV, CA, whole heart labels	Hybrid segmentations using advanced neural implicit representations
CCA-200	200	Internal diameter, vectorized meshes	Mesh annotations, fine morphology (Yang et al., 2023)

These repositories provide high-resolution images, manual or semi-automated segmentations, surface meshes, and centerline data, serving as standard benchmarks for segmentation, morphology analysis, and mesh-based computational modeling.

A distinctive feature of the most recent public datasets is curation for anatomical coherence, explicit marking of data defects (e.g., step artifacts, limited FOV), and synthesis of complex cardiac regions such as the left atrial appendage and pulmonary veins in addition to the coronary arteries (Hansen et al., 7 Oct 2025).

3. Segmentation and 3D Reconstruction Methodologies

Multiple segmentation strategies have been reported to leverage the spatial richness of high-resolution CCTA:

(a) Traditional Multi-Stage Model-based Methods:

Such pipelines often involve vessel enhancement filtering (e.g., Frangi Vesselness), centerline extraction with minimum cost path algorithms, intensity-based membership functions (generalized bell/sigmoid), and region-based level set segmentation (incorporating priors) for lumen, outer wall, and plaque. Final 3D surfaces are constructed using marching cubes, yielding meshes of lumen, wall, and calcified plaque (Kigka et al., 2019).

(b) Deep Architectures (U-Net Variants, Attention and Hybrid Models):

State-of-the-art deep learning solutions include 3D U-Net and attention-augmented networks (e.g., AGFA-Net with FRM/SAFA/HFIM modules) that maximize multi-scale feature aggregation, channel/spatial self-attention, and hierarchical integration for improved sensitivity to low-contrast small vessels, complex anatomy, and inter-subject variability (Liu et al., 13 Jun 2024, Yao et al., 27 Apr 2025).

(c) Geometry-based Cascaded and Mesh-Deformation Networks:

To directly exploit the non-Euclidean structure of coronary vessels, geometry-based cascaded neural approaches deform template meshes via graph convolutional networks, regularized by classifying morphologies (tube, bifurcation), and optimize loss functions such as Chamfer and Laplacian smoothness, resulting in continuous, fragmentation-free surface reconstructions (Yang et al., 2023).

(d) Multi-task and Clinical Decision Networks:

Tabular models (TabPerceiver) combine imaging-derived segmental stenosis, clinical metadata, and CCTA features to jointly predict coronary artery disease risk and recommend downstream diagnostic or therapeutic interventions (Lu et al., 2023).

4. Quantitative Validation and Performance Metrics

Evaluation metrics in CCTA research are standardized:

• Dice Similarity Coefficient (DSC): $DSC = \frac{2|A \cap B|}{|A|+|B|}$ is routinely computed for vessel mask overlap; values >80% are considered strong for multi-expert datasets (Zeng et al., 2022, Gharleghi et al., 2022, Yang et al., 2023, Liu et al., 13 Jun 2024, Yao et al., 27 Apr 2025).
• Hausdorff Distance (HD/HD95): Quantifies maximum boundary error; lower HD (e.g., HD ≈ 0.23 mm (Liu et al., 13 Jun 2024), HD95 < 7 mm (Gharleghi et al., 2022, Yao et al., 27 Apr 2025)) signals accurate geometric reconstruction.
• Centerline/Topological Error: Number of fragmented segments (“NoS”), Chamfer distance, and specialized overlap metrics (e.g., Ot(d) (Wang et al., 19 Jul 2024)) evaluate vascular continuity and tree connectivity.
• Stenosis Assessment: Derived via cross-sectional area and diameter quantification along extracted centerlines, providing direct clinical grading (e.g., $$b = \left(1 - \frac{A_{\min}}{A_{\text{ref}}}\right) \times 100\%$$ (Yao et al., 27 Apr 2025)).
• Plaque Validation: Spatial and quantitative agreement against intravascular ultrasound (IVUS), with reported metric correlations for degree of stenosis, plaque burden, and minimal lumen area $r = 0.75$–0.85 (Kigka et al., 2019).

An average interobserver DSC of 85.6% and HD95 of 5.9 mm indicates reliable ground truth in expertly curated CCTA datasets (Gharleghi et al., 2022).

5. Challenges, Limitations, and Data Quality Considerations

High-resolution CCTA data present several technical and biological challenges:

• Motion and Field-of-View Artifacts: Due to cardiac/respiratory motion, even photon-counting CT (PCCT) demands residual motion <0.4 mm to prevent blurring of fine features (Li et al., 2023). Limited FOV causes incomplete capture of the left atrial appendage and other structures in a substantial fraction of public scans (Hansen et al., 7 Oct 2025).
• Contrast and Temporal Variability: PCAT assessment and radiomic analysis are sensitive to iodine bolus timing. Small delays (±2 s) cause significant shifts in HU and observed PCAT volume (≈15% volume drop), potentially confounding biomarker studies (Wu et al., 2023).
• Image Contrast and Segmentation Limits: Low tissue-vessel contrast, small branch diameters (<1 mm), and variable plaque burden challenge even advanced learning models, leading to potential fragmentation or over-smoothing. Specialized attention or myocardial region guidance modules mitigate some of these pitfalls (Yao et al., 27 Apr 2025).
• Ground Truth Limitations: Even with three-expert consensus and majority voting, fine details (tiny branches, distal segments) may suffer from annotation limitations and variable interobserver agreement, necessitating robust validation procedures (Gharleghi et al., 2022).

6. Applications in Clinical and Computational Science

High-resolution CCTA data underpin a range of translational research and clinical activities:

• Automated Quantitative Analysis: End-to-end pipelines for segmentation, stenosis detection, and plaque quantification allow non-invasive, objective CAD assessment and personalized risk stratification (Candemir et al., 2019, Yao et al., 27 Apr 2025).
• Computational Fluid Dynamics (CFD) and Device Testing: Accurate vessel meshes and lumen/wall reconstructions support simulation of blood flow, device deployment (e.g., stents), and forecasting of adverse events (Gharleghi et al., 2022, Hansen et al., 7 Oct 2025).
• 3D Printing and Education: Patient-specific coronary models generated using high-resolution mesh data facilitate educational, pre-surgical planning, and hands-on training applications.
• Multi-Modal Image Registration: Advanced registration frameworks enable alignment and fusion of CCTA with intravascular imaging (OCT, IVUS), leveraging virtual catheter path optimization to achieve near pixel-wise alignment despite non-rigid distortions (Kadry et al., 2022).
• Foundation Model Synthesis and Cross-Modal Learning: The integration of diffusion models, image foundation models, and transformer architectures allows for generative data augmentation, robust correspondence matching, and image translation across modalities, addressing data scarcity and improving model generalization (Zhao et al., 31 Mar 2025).

7. Future Directions and Open Problems

Several avenues of continued research and development are emerging:

• Spectral and Ultra-High-Resolution Imaging: Ongoing evaluation of PCCT, advanced denoising, and dose-reduction strategies will further enable characterization of vulnerable plaque components and tissue composition at unprecedented resolution (Li et al., 2023).
• Robustness to Artifacts and Anatomical Variation: New segmentation and registration methods must better handle field-of-view constraints, motion artifacts, and naturally occurring anatomical variability, especially in underrepresented demographics.
• Multi-Modal, Multi-Center, and Federated Analyses: Domain adaptation, federated learning, and harmonization are needed to translate algorithmic gains into large-scale, reproducible, multi-center clinical settings (Zeng et al., 2022).
• Standardized Quality Control and Reporting: The inclusion of explicit quality control markers in public datasets facilitates systematic exclusion of artifact-affected scans and supports more rigorous benchmarking (Hansen et al., 7 Oct 2025).
• Interpretability and Clinical Integration: The field is incorporating uncertainty quantification, visually interpretable saliency maps, and integrated anatomical priors, advancing explainable AI for clinician-in-the-loop adoption (Gerbasi et al., 2023, Yao et al., 27 Apr 2025).

High-resolution CCTA data remain a cornerstone for precision cardiovascular imaging, computational simulation, and automated diagnosis, with ongoing innovation in acquisition protocols, dataset curation, algorithmic frameworks, and integrated clinical workflows continually expanding their relevance and impact.