title: X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
    Tomographic Reconstruction
summary: Four-dimensional computed tomography (4D CT) reconstruction is crucial for
  capturing dynamic anatomical changes but faces inherent limitations from
  conventional phase-binning workflows. Current methods discretize temporal
  resolution into fixed phases with respiratory gating devices, introducing
  motion misalignment and restricting clinical practicality. In this paper, We
  propose X^2-Gaussian, a novel framework that enables continuous-time 4D-CT
  reconstruction by integrating dynamic radiative Gaussian splatting with
  self-supervised respiratory motion learning. Our approach models anatomical
  dynamics through a spatiotemporal encoder-decoder architecture that predicts
  time-varying Gaussian deformations, eliminating phase discretization. To remove
  dependency on external gating devices, we introduce a physiology-driven
  periodic consistency loss that learns patient-specific breathing cycles
  directly from projections via differentiable optimization. Extensive
  experiments demonstrate state-of-the-art performance, achieving a 9.93 dB PSNR
  gain over traditional methods and 2.25 dB improvement against prior Gaussian
  splatting techniques. By unifying continuous motion modeling with hardware-free
  period learning, X^2-Gaussian advances high-fidelity 4D CT reconstruction for
  dynamic clinical imaging. Project website at: https://x2-gaussian.github.io/.
paper_authors._id: 67e69b75113f7c9e552bea69
paper_authors.name: Weihao Yu
paper_authors.status: claimed_verified
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paper_authors.fullname: vortexyu
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paper_authors.user: vortex778
paper_authors._id: 67e69b75113f7c9e552bea6a
paper_authors.name: Yuanhao Cai
paper_authors.status: admin_assigned
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paper_authors.fullname: Yuanhao Cai
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paper_authors.user: CaiYuanhao
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paper_authors.name: Ruyi Zha
paper_authors._id: 67e69b75113f7c9e552bea6c
paper_authors.name: Zhiwen Fan
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paper_authors.fullname: fan
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paper_authors.user: Fanzhiwen
paper_authors._id: 67e69b75113f7c9e552bea6d
paper_authors.name: Chenxin Li
paper_authors.status: admin_assigned
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paper_authors.fullname: Chenxin Li
paper_authors.type: user
paper_authors.user: XGGNet
paper_authors._id: 67e69b75113f7c9e552bea6e
paper_authors.name: Yixuan Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T10:00:15.694Z
paper_authors._id: 640fdfc9f2d7c41a1ea112ef
paper_authors.avatarUrl: /avatars/780328c388ac4bc9acdf063e7833259d.svg
paper_authors.fullname: yxyuan
paper_authors.type: user
paper_authors.user: yixuanyuan
paper_title: X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
    Tomographic Reconstruction
paper_summary: Four-dimensional computed tomography (4D CT) reconstruction is crucial for
  capturing dynamic anatomical changes but faces inherent limitations from
  conventional phase-binning workflows. Current methods discretize temporal
  resolution into fixed phases with respiratory gating devices, introducing
  motion misalignment and restricting clinical practicality. In this paper, We
  propose X^2-Gaussian, a novel framework that enables continuous-time 4D-CT
  reconstruction by integrating dynamic radiative Gaussian splatting with
  self-supervised respiratory motion learning. Our approach models anatomical
  dynamics through a spatiotemporal encoder-decoder architecture that predicts
  time-varying Gaussian deformations, eliminating phase discretization. To remove
  dependency on external gating devices, we introduce a physiology-driven
  periodic consistency loss that learns patient-specific breathing cycles
  directly from projections via differentiable optimization. Extensive
  experiments demonstrate state-of-the-art performance, achieving a 9.93 dB PSNR
  gain over traditional methods and 2.25 dB improvement against prior Gaussian
  splatting techniques. By unifying continuous motion modeling with hardware-free
  period learning, X^2-Gaussian advances high-fidelity 4D CT reconstruction for
  dynamic clinical imaging. Project website at: https://x2-gaussian.github.io/.
paper_ai_summary: X$^2$-Gaussian framework achieves high-fidelity 4D CT reconstruction through continuous-time modeling and self-supervised respiratory motion learning without external gating devices.
paper_ai_keywords: Gaussian splatting spatiotemporal encoder-decoder architecture Gaussian deformations physiology-driven periodic consistency loss differentiable optimization