用非增强CT生成数字对比增强CT，帮高危患者避免造影剂风险。
Digital Contrast CT Pulmonary Angiography Synthesis from Non-contrast CT for Pulmonary Vascular Disease
- 用级联循环GAN模型从非增强CT合成数字对比增强CT。
- 合成图像在血管增强和结构保真度上优于现有方法，测试集MAE仅165.12。
- 适用于肺血管分割与量化，小血管显示效果显著提升。
计算机断层扫描肺动脉造影（CTPA）是诊断肺血管疾病（如肺栓塞和慢性血栓栓塞性肺高血压）的金标准，但依赖碘对比剂存在肾毒性及过敏反应风险，尤其对高危患者。本研究提出一种基于级联循环生成对抗网络（CycleGAN）的方法，从非增强CT（NCCT）生成数字对比增强CT（DCCTPA）。共收集来自三个中心的410对配对数据，其中249对用于内部训练与验证，161对作为测试集评估模型泛化能力及下游临床任务表现。相比当前最优方法，该模型在定量指标上表现最佳：验证集上平均绝对误差（MAE）为156.28，峰值信噪比（PSNR）为20.71，结构相似性（SSIM）为0.98；测试集上对应值分别为165.12、20.27和0.98，且定性视觉效果优异，有效增强血管并保持结构完整性。该方法进一步应用于肺血管分割与量化任务，在测试集上动脉与静脉分割的平均Dice、clDice和clRecall分别达到0.70/0.71/0.73与0.70/0.72/0.75，显著优于原始NCCT输入。不同阅片者间血管体积测量的组内相关系数（ICC）在DCCTPA与CTPA之间达0.81，显著高于NCCT与CTPA间的0.70，表明其在小血管增强方面具有显著优势。
原文摘要 · Abstract (English)
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.
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