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.

翻译：摘要：四维数字减影血管造影（4D DSA）是一种医学成像技术，通过采集造影剂填充血管过程中不同时相和角度的系列二维图像，对脑血管疾病的诊断具有重要作用。在稀疏采样条件下提升渲染质量与速度，对于观察病灶状态和定位至关重要。现有方法在稀疏视角下渲染质量不足且速度较慢。为解决上述局限，我们提出TOGS——一种具有时变不透明度偏移的高斯泼溅方法，可有效提升4D DSA的渲染质量与速度。我们为每个高斯体引入不透明度偏移表，以建模造影剂辐射的时域变化。通过插值不透明度偏移表，可确定高斯体在不同时刻的不透明度变化，进而实现对应时刻二维DSA图像的渲染。此外，我们在损失函数中引入平滑损失项，以缓解稀疏视角场景下模型可能出现的过拟合问题。训练阶段通过随机修剪高斯体降低模型的存储开销。实验结果表明，与先前方法相比，本模型在相同训练视角数下实现了最先进的重建质量，同时支持实时渲染并保持低存储开销。代码将公开提供。