发表机构
Delft University of Technology; Fraunhofer IGD（代尔夫特理工大学; 弗劳恩霍夫计算机图形研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于3D高斯泼溅的谱CT重建与基础材料分解方法，通过联合优化能量相关基础函数和材料分数，实现高精度重建与分解，优于传统方法。
AI 中文摘要
谱计算机断层扫描（CT）通过测量多个能量通道的衰减来扩展传统CT，从而改进对物理X射线相互作用和能量依赖性材料行为的建模，并带来更丰富的场景理解。我们提出了一种新颖的谱CT重建和基础材料分解方法，该方法使用3D高斯泼溅，通过将每个高斯基基础材料分数添加到可学习参数集中，这些参数与一组能量依赖性基础函数共同定义了整个谱范围内的衰减。基础函数代表各种物理衰减模型，如光电吸收和康普顿散射，并通过可微分的多色前向模型在所有能量通道上联合优化，材料分解通过对所得系数进行均值漂移聚类来实现。我们在一个基线真实世界数据集以及我们引入的一个合成数据集上评估了我们的方法，并与传统重建算法和最先进的基于学习的CT重建方法进行了比较。我们的方法在新视图合成方面优于所有传统基线，并在谱CT体积重建的所有比较方法中实现了最佳PSNR，同时用单个共享表示描述所有能量通道，所需的高斯数量与基于单通道高斯泼溅的CT重建方法相当。对于基础材料分解，没有传统或基于学习的基线提供一步分解和直接RGB材料分割，我们的方法还以比传统流程更高的PSNR恢复了光电基础。
英文摘要
Spectral computed tomography (CT) extends conventional CT by measuring attenuation across multiple energy channels, allowing improved modeling of physical X-ray interactions and energy-dependent material behavior and leading to richer scene understanding. We present a novel method for spectral CT reconstruction and basis material decomposition using 3D Gaussian Splatting by adding per-Gaussian basis material fractions to the set of learnable parameters, which together with a set of energy-dependent basis functions define the attenuation across the full spectral range. The basis functions represent various physical attenuation models such as photoelectric absorption and Compton scattering, and are jointly optimized across all energy channels through a differentiable polychromatic forward model, with material decomposition performed via mean-shift clustering of the resulting coefficients. We evaluate our method on a baseline real-world dataset as well as a synthetic dataset that we introduce, comparing against traditional reconstruction algorithms and state-of-the-art learning-based CT reconstruction methods. Our approach outperforms all traditional baselines in novel view synthesis and achieves the best PSNR among all compared methods for spectral CT volume reconstruction, while describing all energy channels with a single shared representation that requires a number of Gaussians comparable to single-channel Gaussian splatting-based CT reconstruction approaches. For basis material decomposition, no traditional or learning-based baseline offers one-step decomposition with direct RGB material segmentation, and our method additionally recovers the photoelectric basis with higher PSNR than traditional pipelines.
