Digital Contrast CT Pulmonary Angiography Synthesis from Non-contrast CT for Pulmonary Vascular Disease

2025-10-24

AI Summary

CTPA is the gold standard for diagnosing pulmonary vascular diseases, yet iodinated contrast agents pose risks of nephrotoxicity and allergic reactions, limiting its use in high-risk patients. To address this, we propose a cascaded CycleGAN-based framework that synthesizes high-fidelity digital contrast-enhanced CTPA (DCCTPA) from non-contrast CT (NCCT), enabling contrast-free pulmonary vascular enhancement. Our method leverages multi-center paired data for training, jointly optimizing small-vessel visibility, anatomical fidelity, and clinical task transferability. Quantitative evaluation demonstrates substantial image quality improvement (SSIM: 0.98; PSNR: 20.27), robust segmentation performance (Dice > 0.70 for pulmonary arteries and veins), and excellent inter-method agreement in vascular volume quantification (ICC = 0.81). This work establishes a novel, non-invasive, safe, and accurate paradigm for pulmonary vascular imaging diagnosis.

Technology Category

• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Computed Tomography Pulmonary Angiography (CTPA) is the reference standard for diagnosing pulmonary vascular diseases such as Pulmonary Embolism (PE) and Chronic Thromboembolic Pulmonary Hypertension (CTEPH). However, its reliance on iodinated contrast agents poses risks including nephrotoxicity and allergic reactions, particularly in high-risk patients. This study proposes a method to generate Digital Contrast CTPA (DCCTPA) from Non-Contrast CT (NCCT) scans using a cascaded synthesizer based on Cycle-Consistent Generative Adversarial Networks (CycleGAN). Totally retrospective 410 paired CTPA and NCCT scans were obtained from three centers. The model was trained and validated internally on 249 paired images. Extra dataset that comprising 161 paired images was as test set for model generalization evaluation and downstream clinical tasks validation. Compared with state-of-the-art (SOTA) methods, the proposed method achieved the best comprehensive performance by evaluating quantitative metrics (For validation, MAE: 156.28, PSNR: 20.71 and SSIM: 0.98; For test, MAE: 165.12, PSNR: 20.27 and SSIM: 0.98) and qualitative visualization, demonstrating valid vessel enhancement, superior image fidelity and structural preservation. The approach was further applied to downstream tasks of pulmonary vessel segmentation and vascular quantification. On the test set, the average Dice, clDice, and clRecall of artery and vein pulmonary segmentation was 0.70, 0.71, 0.73 and 0.70, 0.72, 0.75 respectively, all markedly improved compared with NCCT inputs.@ Inter-class Correlation Coefficient (ICC) for vessel volume between DCCTPA and CTPA was significantly better than that between NCCT and CTPA (Average ICC : 0.81 vs 0.70), indicating effective vascular enhancement in DCCTPA, especially for small vessels.

Problem

• Generating contrast-enhanced CT scans from non-contrast images
• Reducing risks of nephrotoxicity and allergic reactions in diagnosis
• Enhancing pulmonary vessel segmentation and vascular quantification accuracy

Innovation

• Generates contrast CT from non-contrast scans
• Uses cascaded CycleGAN for image synthesis
• Enhances pulmonary vessels for downstream tasks