Discretized Gaussian Representation for Tomographic Reconstruction

• URL: http://arxiv.org/abs/2411.04844v2
• Date: Wed, 11 Dec 2024 17:40:32 GMT
• Title: Discretized Gaussian Representation for Tomographic Reconstruction
• Authors: Shaokai Wu, Yuxiang Lu, Wei Ji, Suizhi Huang, Fengyu Yang, Shalayiding Sirejiding, Qichen He, Jing Tong, Yanbiao Ji, Yue Ding, Hongtao Lu,
• Abstract summary: We propose a novel Discretized Gaussian Representation (DGR) specifically designed for Computed Tomography (CT) reconstruction. Our representation directly reconstructs the 3D volume using a set of discretized Gaussian functions in an end-to-end manner.
• Score: 20.390232991700977
• License:
• Abstract: 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.

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