Differentiable Gaussian Representation for Incomplete CT Reconstruction

• URL: http://arxiv.org/abs/2411.04844v1
• Date: Thu, 07 Nov 2024 16:32:29 GMT
• Title: Differentiable Gaussian Representation for Incomplete CT 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 Gaussian Representation for Incomplete CT Reconstruction (GRCT) without the usage of any neural networks or full-dose CT data. Our method can be applied to multiple views and angles without changing the architecture. Experiments on multiple datasets and settings demonstrate significant improvements in reconstruction quality metrics and high efficiency.
• Score: 20.390232991700977
• License:
• Abstract: Incomplete Computed Tomography (CT) benefits patients by reducing radiation exposure. However, reconstructing high-fidelity images from limited views or angles remains challenging due to the ill-posed nature of the problem. Deep Learning Reconstruction (DLR) methods have shown promise in enhancing image quality, but the paradox between training data diversity and high generalization ability remains unsolved. In this paper, we propose a novel Gaussian Representation for Incomplete CT Reconstruction (GRCT) without the usage of any neural networks or full-dose CT data. Specifically, we model the 3D volume as a set of learnable Gaussians, which are optimized directly from the incomplete sinogram. Our method can be applied to multiple views and angles without changing the architecture. Additionally, we propose a differentiable Fast CT Reconstruction method for efficient clinical usage. Extensive experiments on multiple datasets and settings demonstrate significant improvements in reconstruction quality metrics and high efficiency. We plan to release our code as open-source.

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