{"ID":2877342,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21154","arxiv_id":"2508.21154","title":"RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration","abstract":"Computed Tomography (CT)/X-ray registration in image-guided navigation remains challenging because of its stringent requirements for high accuracy and real-time performance. Traditional \"render and compare\" methods, relying on iterative projection and comparison, suffer from spatial information loss and domain gap. 3D reconstruction from biplanar X-rays supplements spatial and shape information for 2D/3D registration, but current methods are limited by dense-view requirements and struggles with noisy X-rays. To address these limitations, we introduce RadGS-Reg, a novel framework for vertebral-level CT/X-ray registration through joint 3D Radiative Gaussians (RadGS) reconstruction and 3D/3D registration. Specifically, our biplanar X-rays vertebral RadGS reconstruction module explores learning-based RadGS reconstruction method with a Counterfactual Attention Learning (CAL) mechanism, focusing on vertebral regions in noisy X-rays. Additionally, a patient-specific pre-training strategy progressively adapts the RadGS-Reg from simulated to real data while simultaneously learning vertebral shape prior knowledge. Experiments on in-house datasets demonstrate the state-of-the-art performance for both tasks, surpassing existing methods. The code is available at: https://github.com/shenao1995/RadGS_Reg.","short_abstract":"Computed Tomography (CT)/X-ray registration in image-guided navigation remains challenging because of its stringent requirements for high accuracy and real-time performance. Traditional \"render and compare\" methods, relying on iterative projection and comparison, suffer from spatial information loss and domain gap. 3D...","url_abs":"https://arxiv.org/abs/2508.21154","url_pdf":"https://arxiv.org/pdf/2508.21154v1","authors":"[\"Ao Shen\",\"Xueming Fu\",\"Junfeng Jiang\",\"Qiang Zeng\",\"Ye Tang\",\"Zhengming Chen\",\"Luming Nong\",\"Feng Wang\",\"S. Kevin Zhou\"]","published":"2025-08-28T18:40:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":610368,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2877342,"paper_url":"https://arxiv.org/abs/2508.21154","paper_title":"RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration","repo_url":"https://github.com/shenao1995/RadGS_Reg","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}