Abstract
Accurate 3D aortic construction is crucial for clinical diagnosis, preoperative planning, and computational fluid dynamics (CFD) simulations, as it enables the estimation of critical hemodynamic parameters such as blood flow velocity, pressure distribution, and wall shear stress. Existing construction methods often rely on large annotated training datasets and extensive manual intervention. While the resulting meshes can serve for visualization purposes, they struggle to produce geometrically consistent, well-constructed surfaces suitable for downstream CFD analysis. To address these challenges, we introduce AortaDiff, a diffusion-based framework that generates smooth aortic surfaces directly from CT/MRI volumes. AortaDiff first employs a volume-guided conditional diffusion model (CDM) to iteratively generate aortic centerlines conditioned on volumetric medical images. Each centerline point is then automatically used as a prompt to extract the corresponding vessel contour, ensuring accurate boundary delineation. Finally, the extracted contours are fitted into a smooth 3D surface, yielding a continuous, CFD-compatible mesh representation. AortaDiff offers distinct advantages over existing methods, including an end-to-end workflow, minimal dependency on large labeled datasets, and the ability to generate CFD-compatible aorta meshes with high geometric fidelity. Experimental results demonstrate that AortaDiff performs effectively even with limited training data, successfully constructing both normal and pathologically altered aorta meshes, including cases with aneurysms or coarctation. This capability enables the generation of high-quality visualizations and positions AortaDiff as a practical solution for cardiovascular research.
Abstract (translated)
精确的3D主动脉构建对于临床诊断、术前规划和计算流体动力学（CFD）模拟至关重要，因为它能够估计诸如血流速度、压力分布和血管剪切应力等关键血液动力学参数。现有的构建方法通常依赖于大量标注的数据集以及大量的手动干预。虽然由此产生的网格可以用于可视化目的，但它们在生成几何一致性良好且适合后续CFD分析的表面方面存在困难。 为了应对这些挑战，我们引入了AortaDiff，这是一个基于扩散框架的方法，可以直接从CT/MRI体积数据中生成平滑的主动脉表面。AortaDiff首先采用一种由体素引导的条件扩散模型（CDM），以迭代方式根据体积医学图像生成主动脉中心线。然后自动使用每个中心线点作为提示来提取相应的血管轮廓，确保边界界定准确。最后，将提取出的轮廓拟合为一个平滑的3D表面，从而产生连续且适合CFD分析的网格表示。 AortaDiff相对于现有方法具有明显优势，包括端到端的工作流程、对大规模标注数据集依赖性小以及能够生成几何精度高且与CFD兼容的主动脉网格。实验结果表明，即使在训练数据有限的情况下，AortaDiff也能有效地构建正常和病理改变（如动脉瘤或狭窄）的主动脉网格。这一能力使得高质量可视化得以实现，并将AortaDiff定位为心血管研究中的实用解决方案。
URL
https://arxiv.org/abs/2507.13404
