用离散高斯函数直接重建CT三维图像，速度快质量高
Discretized Gaussian Representation for Tomographic Reconstruction
- 用离散高斯函数建模3D体积，端到端直接重建
- 在真实与合成数据上均优于现有方法，速度更快
- 适合需要快速高质量重建的医学影像场景
计算机断层扫描（CT）可实现高精度横截面成像，但重建质量与计算效率难以兼顾。深度学习方法虽显著提升图像质量和降噪效果，却依赖大规模训练数据且计算开销大。近年来的场景重建技术如神经辐射场和3D高斯溅射提供了新思路，但不适用于直接的体数据CT重建。本文提出离散高斯表示（DGR），一种使用离散高斯函数集直接端到端重建3D体积的新框架。为进一步提升效率，引入快速体重建技术，通过高度并行化将高斯贡献聚合至体素网格，开销极小。在真实世界和合成数据集上的大量实验表明，DGR在多种CT重建场景中均实现更优的重建质量与运行性能。代码已公开于https://github.com/wskingdom/DGR。
原文摘要 · Abstract (English)
Computed Tomography (CT) enables detailed cross-sectional imaging but continues to face challenges in balancing reconstruction quality and computational efficiency. While deep learning-based methods have significantly improved image quality and noise reduction, they typically require large-scale training data and intensive computation. Recent advances in scene reconstruction, such as Neural Radiance Fields and 3D Gaussian Splatting, offer alternative perspectives but are not well-suited for direct volumetric CT reconstruction. In this work, we propose Discretized Gaussian Representation (DGR), a novel framework that reconstructs the 3D volume directly using a set of discretized Gaussian functions in an end-to-end manner. To further enhance efficiency, we introduce Fast Volume Reconstruction, a highly parallelized technique that aggregates Gaussian contributions into the voxel grid with minimal overhead. Extensive experiments on both real-world and synthetic datasets demonstrate that DGR achieves superior reconstruction quality and runtime performance across various CT reconstruction scenarios. Our code is publicly available at https://github.com/wskingdom/DGR.
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