4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images
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.
从稀疏视角的动态数字减影血管造影(DSA)图像中重建三维血管结构,可以在降低辐射剂量的同时实现准确的医学评估。然而,现有方法常常结果欠佳或计算耗时过长。为此,我们提出了 四维辐射高斯点云(4D Radiative Gaussian Splatting, 4DRGS),以高效实现高质量重建。 具体而言,我们使用四维辐射高斯核来表示血管结构。每个高斯核具有时间不变的几何参数,包括位置、旋转和尺度,用于建模静态的血管结构。为了捕捉对比剂流动的时间变化响应,我们通过一个紧凑的神经网络预测每个高斯核的时间相关中心衰减。我们通过 X 射线光栅化对这些高斯核进行点云渲染,合成 DSA 图像,并利用真实采集的 DSA 图像优化模型。最终的三维血管体积从经过充分训练的高斯核体素化生成。 此外,我们引入了 累积衰减修剪 和 有界缩放激活 策略,以进一步提升重建质量。基于真实患者数据的大量实验表明,4DRGS 在仅 5 分钟的训练时间内即可实现卓越的重建效果,其速度比当前最先进方法快 32 倍。这表明 4DRGS 在实际临床应用中具有巨大潜力。