Reconstructing 3D vessel structures from sparse-view dynamic digital subtraction angiography (DSA) images enables accurate medical assessment while reducing radiation exposure. Existing methods often produce suboptimal results or require excessive computation time. In this work, we propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently. In detail, we represent the vessels with 4D radiative Gaussian kernels. Each kernel has time-invariant geometry parameters, including position, rotation, and scale, to model static vessel structures. The time-dependent central attenuation of each kernel is predicted from a compact neural network to capture the temporal varying response of contrast agent flow. We splat these Gaussian kernels to synthesize DSA images via X-ray rasterization and optimize the model with real captured ones. The final 3D vessel volume is voxelized from the well-trained kernels. Moreover, we introduce accumulated attenuation pruning and bounded scaling activation to improve reconstruction quality. Extensive experiments on real-world patient data demonstrate that 4DRGS achieves impressive results in 5 minutes training, which is 32x faster than the state-of-the-art method. This underscores the potential of 4DRGS for real-world clinics.

翻译：从稀疏视角动态数字减影血管造影（DSA）图像重建三维血管结构，能够在降低辐射暴露的同时实现精确的医学评估。现有方法常产生次优结果或需要过长的计算时间。本工作提出四维辐射高斯泼溅（4DRGS）方法，以实现高效的高质量重建。具体而言，我们使用四维辐射高斯核来表示血管。每个核具有时间不变的几何参数（包括位置、旋转和尺度）以建模静态血管结构。每个核的时间依赖性中心衰减由一个紧凑的神经网络预测，以捕捉对比剂流动的时变响应。我们通过X射线光栅化将这些高斯核泼溅合成为DSA图像，并利用真实采集的图像对模型进行优化。最终的三维血管体素网格从训练良好的高斯核体素化得到。此外，我们引入了累积衰减剪枝和有界缩放激活机制以提升重建质量。在真实世界患者数据上进行的大量实验表明，4DRGS在5分钟的训练时间内取得了令人印象深刻的结果，比现有最优方法快32倍。这凸显了4DRGS在实际临床应用中的潜力。