Computed Tomography (CT) is a widely used imaging technique that provides detailed cross-sectional views of objects. Over the past decade, Deep Learning-based Reconstruction (DLR) methods have led efforts to enhance image quality and reduce noise, yet they often require large amounts of data and are computationally intensive. Inspired by recent advancements in scene reconstruction, some approaches have adapted NeRF and 3D Gaussian Splatting (3DGS) techniques for CT reconstruction. However, these methods are not ideal for direct 3D volume reconstruction. In this paper, we reconsider the representation of CT reconstruction and propose a novel Discretized Gaussian Representation (DGR) specifically designed for CT. Unlike the popular 3D Gaussian Splatting, our representation directly reconstructs the 3D volume using a set of discretized Gaussian functions in an end-to-end manner. Additionally, we introduce a Fast Volume Reconstruction technique that efficiently aggregates the contributions of these Gaussians into a discretized volume. Extensive experiments on both real-world and synthetic datasets demonstrate the effectiveness of our method in improving reconstruction quality and computational efficiency. Our code has been provided for review purposes and will be made publicly available upon acceptance.

翻译：计算机断层扫描（CT）是一种广泛应用的成像技术，能够提供物体的详细横截面视图。在过去十年中，基于深度学习的重建方法致力于提升图像质量并降低噪声，但这些方法通常需要大量数据且计算成本高昂。受场景重建领域最新进展的启发，部分研究将神经辐射场与三维高斯泼溅技术应用于CT重建。然而，这些方法并不适用于直接的三维体数据重建。本文重新审视了CT重建的表征方式，提出了一种专为CT设计的新型离散化高斯表示方法。与主流的三维高斯泼溅技术不同，我们的表征方法通过一组离散化高斯函数以端到端方式直接重建三维体数据。此外，我们引入了一种快速体数据重建技术，能够高效地将这些高斯函数的贡献聚合到离散化体数据中。在真实数据集与合成数据集上的大量实验表明，该方法在提升重建质量与计算效率方面具有显著优势。本研究的代码已提供评审使用，将在论文录用后公开。