用2D X光和生理数据生成高保真3D肺部CT，助力重症肺炎管理
High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models
- 基于分数的3D残差扩散模型，从2D X光和生理参数合成3D CT
- 生成的3D CT与真实图像匹配度高，可用于评估肺通气与治疗效果
- 适合重症监护、医学影像生成及远程诊疗场景
急性呼吸窘迫综合征（ARDS）是一种以肺部炎症和呼吸衰竭为特征的严重疾病，病死率约40%。传统胸片仅提供二维视图，难以全面评估肺部病理。三维（3D）计算机断层扫描（CT）能更完整地呈现肺通气、肺不张及治疗效果，但因危重患者转运至远程扫描仪存在实际困难与风险，常规使用受限。本研究采用基于分数的3D残差扩散模型，从2D生成的X光图像及关联生理参数中合成高保真3D肺部CT。初步结果表明，该方法可生成与真实数据高度匹配的高质量3D CT图像，为提升ARDS临床管理提供了有前景的新方案。
原文摘要 · Abstract (English)
Acute respiratory distress syndrome (ARDS) is a severe condition characterized by lung inflammation and respiratory failure, with a high mortality rate of approximately 40%. Traditional imaging methods, such as chest X-rays, provide only two-dimensional views, limiting their effectiveness in fully assessing lung pathology. Three-dimensional (3D) computed tomography (CT) offers a more comprehensive visualization, enabling detailed analysis of lung aeration, atelectasis, and the effects of therapeutic interventions. However, the routine use of CT in ARDS management is constrained by practical challenges and risks associated with transporting critically ill patients to remote scanners. In this study, we synthesize high-fidelity 3D lung CT from 2D generated X-ray images with associated physiological parameters using a score-based 3D residual diffusion model. Our preliminary results demonstrate that this approach can produce high-quality 3D CT images that are validated with ground truth, offering a promising solution for enhancing ARDS management.
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