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。

TOGS: 实时 4D DSA 渲染的时间不透明度偏移高斯喷射

TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D  DSA Rendering

Four-dimensional Digital Subtraction Angiography (4D DSA) plays a critical
role in the diagnosis of many medical diseases, such as Arteriovenous
Malformations (AVM) and Arteriovenous Fistulas (AVF). Despite its significant
application value, the reconstruction of 4D DSA demands numerous views to
effectively model the intricate vessels and radiocontrast flow, thereby
implying a significant radiation dose. To address this high radiation issue, we
propose a Time-aware Attenuation Voxel (TiAVox) approach for sparse-view 4D DSA
reconstruction, which paves the way for high-quality 4D imaging. Additionally,
2D and 3D DSA imaging results can be generated from the reconstructed 4D DSA
images. TiAVox introduces 4D attenuation voxel grids, which reflect attenuation
properties from both spatial and temporal dimensions. It is optimized by
minimizing discrepancies between the rendered images and sparse 2D DSA images.
Without any neural network involved, TiAVox enjoys specific physical
interpretability. The parameters of each learnable voxel represent the
attenuation coefficients. We validated the TiAVox approach on both clinical and
simulated datasets, achieving a 31.23 Peak Signal-to-Noise Ratio (PSNR) for
novel view synthesis using only 30 views on the clinically sourced dataset,
whereas traditional Feldkamp-Davis-Kress methods required 133 views. Similarly,
with merely 10 views from the synthetic dataset, TiAVox yielded a PSNR of 34.32
for novel view synthesis and 41.40 for 3D reconstruction. We also executed
ablation studies to corroborate the essential components of TiAVox. The code
will be publically available.

提出了一种基于时间感知衰减体素 (TiAVox) 的稀疏视图下的四维数字减影血管成像 (4D DSA) 重建方法，通过减少辐射剂量，实现高质量的 4D 成像，同时能够生成二维和三维减影血管成像 (DSA) 图像。