Abstract
Digital subtraction angiography (DSA) is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases. Recent advancements in gaussian splatting and dynamic neural representations have enabled robust 3D vessel reconstruction from sparse dynamic inputs. However, these methods are fundamentally constrained by the resolution of input projections, where performing naive upsampling to enhance rendering resolution inevitably results in severe blurring and aliasing artifacts. Such lack of super-resolution capability prevents the reconstructed 4D models from recovering fine-grained vascular details and intricate branching structures, which restricts their application in precision diagnosis and treatment. To solve this problem, this paper proposes DSA-SRGS, the first super-resolution gaussian splatting framework for dynamic sparse-view DSA reconstruction. Specifically, we introduce a Multi-Fidelity Texture Learning Module that integrates high-quality priors from a fine-tuned DSA-specific super-resolution model, into the 4D reconstruction optimization. To mitigate potential hallucination artifacts from pseudo-labels, this module employs a Confidence-Aware Strategy to adaptively weight supervision signals between the original low-resolution projections and the generated high-resolution pseudo-labels. Furthermore, we develop Radiative Sub-Pixel Densification, an adaptive strategy that leverages gradient accumulation from high-resolution sub-pixel sampling to refine the 4D radiative gaussian kernels. Extensive experiments on two clinical DSA datasets demonstrate that DSA-SRGS significantly outperforms state-of-the-art methods in both quantitative metrics and qualitative visual fidelity.
Abstract (translated)
数字减影血管造影（DSA）是辅助诊断和治疗脑血管疾病的关键成像技术。近年来，高斯点光源技术和动态神经表示的发展使得从稀疏的动态输入中实现稳健的三维血管重建成为可能。然而，这些方法从根本上受到输入投影分辨率的限制：简单地放大以提高渲染分辨率不可避免地会导致严重模糊和混叠伪影。这种缺乏超分辨率能力阻碍了重建的4D模型恢复精细的血管细节和复杂的分支结构，从而限制其在精准诊断和治疗中的应用。 为了解决这个问题，本文提出了一种名为DSA-SRGS（Dynamic Sparse-view DSA Reconstruction with Super-Resolution Gaussian Splats）的首个动态稀疏视图DSA重建超分辨率高斯点光源框架。具体而言，我们引入了一个多保真度纹理学习模块，该模块将精细调优后的特定于DSA的超分辨率模型中的高质量先验知识整合到4D重建优化中。为了减轻伪标签可能带来的幻觉伪影，此模块采用了一种基于信心感知策略自适应地在原始低分辨率投影和生成的高分辨率伪标签之间平衡监督信号。 此外，我们还开发了辐射亚像素密度化技术，这是一种利用从高分辨率子像素采样积累的梯度来优化4D放射高斯核的自适应策略。通过对两个临床DSA数据集进行广泛的实验研究证明，DSA-SRGS在量化指标和定性视觉保真度方面均显著优于现有最先进方法。
URL
https://arxiv.org/abs/2603.04770
