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The abstract reports results on clinical dental scans and three public chest and dental datasets, but does not establish performance across broader clinical settings or clinical outcomes.","speaker":{"name":"Deng, Zihan, Gong, Zhenzhen, Liu, Yujie, Gong, Jiaxin, Liu, Dingkun, Xie, Kexian, Huang, Yixing, Jin, Peng, Niu, Tianye, Wang, Zhisheng, Liu, Fenglin, Shan, Hongming, Yu, Lequan","roleAtSource":"Article author"},"source":{"title":"SparseTokenGS: Gaussian-based Sparse-View CBCT Reconstruction via Reusable Token Pool","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.38360","publishedAt":"2026-10-28","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors say feed-forward Gaussian methods can lose scan-wide information during coordinate decoding because they use local features after the field is constructed.","excerpt":"once the field is fixed, decoding uses only local features and reconstruction quality drops substantially.","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"},{"text":"SparseTokenGS queries a shared reusable token pool at anchors and query coordinates, while retaining the local Gaussian prior.","excerpt":"Anchor Token Fusion and Point-Token Attention query this shared pool at the anchors and at the query coordinates, respectively.","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"},{"text":"The authors report that SparseTokenGS reconstructs the volume in one forward pass, without per-scan optimization or a projection-domain loss.","excerpt":"the volume is reconstructed in one forward pass, without per-scan optimization or a projection-domain loss.","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"},{"text":"On clinical dental scans, the authors report the stated reconstruction metrics using six projections, measured against the full-view reference.","excerpt":"reconstruction from only six projections reaches 29.51 decibels in peak signal-to-noise ratio and a structural similarity of 0.842 against the full-view reference","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"},{"text":"The authors report that off-the-shelf segmentation kept tooth and pulp structures separable for root-canal localization.","excerpt":"off-the-shelf tooth-and-pulp segmentation keeps these structures separable for root-canal localization.","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"},{"text":"The authors present future rapid clinical lesion localization and analysis as a potential benefit, not as a demonstrated clinical outcome.","excerpt":"can benefit future rapid clinical lesion localization and analysis.","url":"https://arxiv.org/abs/2610.38360","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.38360","checkedAt":"2026-11-11T10:54:07.497Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How well does reconstruction and lesion localization hold up across broader clinical scans and settings?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"SparseTokenGS uses a reusable token pool to reconstruct sparse-view CBCT scans","summary":"The authors of SparseTokenGS argue that feed-forward methods for sparse-view cone-beam computed tomography can lose scan-wide information: once a reconstruction field is built, decoding relies on local features. 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