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.
@article{arxiv.2505.15235,
title = {X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography},
author = {Yifan Liu and Wuyang Li and Weihao Yu and Chenxin Li and Alexandre Alahi and Max Meng and Yixuan Yuan},
journal= {arXiv preprint arXiv:2505.15235},
year = {2025}
}