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arXiv AI By Zihan Deng, Zhenzhen Gong, Yujie Liu, Jiaxin Gong, Dingkun Liu, Kexian Xie, Yixing Huang, Peng Jin, Tianye Niu, Zhisheng Wang, Fenglin Liu, Hongming Shan, Lequan Yu

SparseTokenGS: Gaussian-based Sparse-View CBCT Reconstruction via Reusable Token Pool

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SparseTokenGS is a Gaussian-based, feed-forward method for sparse-view cone-beam CT reconstruction that uses a reusable token pool to maintain scan-wide context during decoding. Anchor Token Fusion and Point-Token Attention query this shared pool at anchors and query coordinates, preserving local Gaussian priors while enabling reconstruction in a single forward pass without per-scan optimization. Experiments on chest, dental, volcanic rock, and chicken datasets show that SparseTokenGS achieves superior reconstruction quality, achieving 29.51 dB PSNR and 0.842 SSIM from only six dental projections and maintaining separable tooth-and-pulp structures for root‑canal localization.

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