Four-dimensional Digital Subtraction Angiography (4D DSA) is a medical
imaging technique that provides a series of 2D images captured at different
stages and angles during the process of contrast agent filling blood vessels.
It plays a significant role in the diagnosis of cerebrovascular diseases.
Improving the rendering quality and speed under sparse sampling is important
for observing the status and location of lesions. The current methods exhibit
inadequate rendering quality in sparse views and suffer from slow rendering
speed. To overcome these limitations, we propose TOGS, a Gaussian splatting
method with opacity offset over time, which can effectively improve the
rendering quality and speed of 4D DSA. We introduce an opacity offset table for
each Gaussian to model the temporal variations in the radiance of the contrast
agent. By interpolating the opacity offset table, the opacity variation of the
Gaussian at different time points can be determined. This enables us to render
the 2D DSA image at that specific moment. Additionally, we introduced a Smooth
loss term in the loss function to mitigate overfitting issues that may arise in
the model when dealing with sparse view scenarios. During the training phase,
we randomly prune Gaussians, thereby reducing the storage overhead of the
model. The experimental results demonstrate that compared to previous methods,
this model achieves state-of-the-art reconstruction quality under the same
number of training views. Additionally, it enables real-time rendering while
maintaining low storage overhead. The code will be publicly available.

通过引入 TOGS 方法，使用高斯平铺技术并结合时间上的不透明度偏移，可以提高四维数字减影血管成像的渲染质量和速度，并针对稀疏采样情况降低过拟合问题，实现与先前方法相比的最新重建质量和实时渲染。