X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909622966681600 |
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| author | Liu, Yifan Li, Wuyang Yu, Weihao Li, Chenxin Alahi, Alexandre Meng, Max Yuan, Yixuan |
| author_facet | Liu, Yifan Li, Wuyang Yu, Weihao Li, Chenxin Alahi, Alexandre Meng, Max Yuan, Yixuan |
| contents | Computed Tomography serves as an indispensable tool in clinical workflows, providing non-invasive visualization of internal anatomical structures. Existing CT reconstruction works are limited to small-capacity model architecture and inflexible volume representation. In this work, we present X-GRM (X-ray Gaussian Reconstruction Model), a large feedforward model for reconstructing 3D CT volumes from sparse-view 2D X-ray projections. X-GRM employs a scalable transformer-based architecture to encode sparse-view X-ray inputs, where tokens from different views are integrated efficiently. Then, these tokens are decoded into a novel volume representation, named Voxel-based Gaussian Splatting (VoxGS), which enables efficient CT volume extraction and differentiable X-ray rendering. This combination of a high-capacity model and flexible volume representation, empowers our model to produce high-quality reconstructions from various testing inputs, including in-domain and out-domain X-ray projections. Our codes are available at: https://github.com/CUHK-AIM-Group/X-GRM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography Liu, Yifan Li, Wuyang Yu, Weihao Li, Chenxin Alahi, Alexandre Meng, Max Yuan, Yixuan Image and Video Processing Computer Vision and Pattern Recognition Computed Tomography serves as an indispensable tool in clinical workflows, providing non-invasive visualization of internal anatomical structures. Existing CT reconstruction works are limited to small-capacity model architecture and inflexible volume representation. In this work, we present X-GRM (X-ray Gaussian Reconstruction Model), a large feedforward model for reconstructing 3D CT volumes from sparse-view 2D X-ray projections. X-GRM employs a scalable transformer-based architecture to encode sparse-view X-ray inputs, where tokens from different views are integrated efficiently. Then, these tokens are decoded into a novel volume representation, named Voxel-based Gaussian Splatting (VoxGS), which enables efficient CT volume extraction and differentiable X-ray rendering. This combination of a high-capacity model and flexible volume representation, empowers our model to produce high-quality reconstructions from various testing inputs, including in-domain and out-domain X-ray projections. Our codes are available at: https://github.com/CUHK-AIM-Group/X-GRM. |
| title | X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.15235 |