用AI从2D造影图生成Fontan术后血管3D模型，快速辅助手术规划。
Generative AI Pipeline for Interactive Prompt-driven 2D-to-3D Vascular Reconstruction for Fontan Geometries from Contrast-Enhanced X-Ray Fluoroscopy Imaging
- 用谷歌Gemini和腾讯Hunyuan3D构建多步AI流程，从单视角造影图重建3D几何。
- 16步迭代后生成高保真2D投影，支持快速虚拟血流可视化，耗时少于15分钟。
- 适合心脏外科医生和研究人员，让常规影像也能做复杂血流分析。
针对单心室先天性心脏病的Fontan手术后患者，传统2D造影难以表征复杂的血流模式。当前评估依赖荧光造影，缺乏关键的3D几何信息，限制了计算流体力学（CFD）分析与手术规划。本文开发了一套多步骤AI流程，利用谷歌Gemini 2.5 Flash（25亿参数）通过基于Transformer的神经架构，对荧光造影图像进行系统性、迭代式处理，涵盖医学图像预处理、血管分割、对比增强、伪影去除及2D投影中的虚拟血流可视化。最终视图由腾讯Hunyuan3D-2mini（3.84亿参数）生成立体打印文件。该流程在自定义网页界面中完成，经16步处理，成功从单视角造影生成几何优化的2D投影。初期迭代存在血管特征幻觉，经多次修正后实现解剖结构真实还原。最终投影准确保留复杂Fontan结构，对比度增强，适用于3D转换。AI生成的虚拟血流可视化识别出中央连接处的滞留区及分支动脉的血流模式。全流程耗时低于15分钟，API响应时间达秒级。本方法证明了从常规造影数据生成可进行CFD分析的几何模型的临床可行性，支持3D重建与快速虚拟血流分析，为全面CFD模拟前提供初步洞察。尽管需多次修正以确保精度，但为利用普遍可得的影像数据普及先进几何与血流分析奠定了基础。
原文摘要 · Abstract (English)
Fontan palliation for univentricular congenital heart disease progresses to hemodynamic failure with complex flow patterns poorly characterized by conventional 2D imaging. Current assessment relies on fluoroscopic angiography, providing limited 3D geometric information essential for computational fluid dynamics (CFD) analysis and surgical planning. A multi-step AI pipeline was developed utilizing Google's Gemini 2.5 Flash (2.5B parameters) for systematic, iterative processing of fluoroscopic angiograms through transformer-based neural architecture. The pipeline encompasses medical image preprocessing, vascular segmentation, contrast enhancement, artifact removal, and virtual hemodynamic flow visualization within 2D projections. Final views were processed through Tencent's Hunyuan3D-2mini (384M parameters) for stereolithography file generation. The pipeline successfully generated geometrically optimized 2D projections from single-view angiograms after 16 processing steps using a custom web interface. Initial iterations contained hallucinated vascular features requiring iterative refinement to achieve anatomically faithful representations. Final projections demonstrated accurate preservation of complex Fontan geometry with enhanced contrast suitable for 3D conversion. AI-generated virtual flow visualization identified stagnation zones in central connections and flow patterns in branch arteries. Complete processing required under 15 minutes with second-level API response times. This approach demonstrates clinical feasibility of generating CFD-suitable geometries from routine angiographic data, enabling 3D generation and rapid virtual flow visualization for cursory insights prior to full CFD simulation. While requiring refinement cycles for accuracy, this establishes foundation for democratizing advanced geometric and hemodynamic analysis using readily available imaging data.
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