X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
  Tomographic Reconstruction
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/.
67e69b75113f7c9e552bea69
Weihao Yu
claimed_verified
2025-03-28T14:22:23.190Z
660b9dfc8b022f13fdc8db83
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vortexyu
user
vortex778
67e69b75113f7c9e552bea6a
Yuanhao Cai
admin_assigned
2025-03-31T09:59:39.476Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C2elfn7L68jAt4dtHzDAW.png
Yuanhao Cai
user
CaiYuanhao
67e69b75113f7c9e552bea6b
Ruyi Zha
67e69b75113f7c9e552bea6c
Zhiwen Fan
admin_assigned
2025-03-31T09:59:58.337Z
6526386e1c6a09292d8d0a22
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fan
user
Fanzhiwen
67e69b75113f7c9e552bea6d
Chenxin Li
admin_assigned
2025-03-31T10:00:05.746Z
6421c1cdeaad1bcb28b0e903
/avatars/7c720d0e39536a7e49340052f464a80d.svg
Chenxin Li
user
XGGNet
67e69b75113f7c9e552bea6e
Yixuan Yuan
admin_assigned
2025-03-31T10:00:15.694Z
640fdfc9f2d7c41a1ea112ef
/avatars/780328c388ac4bc9acdf063e7833259d.svg
yxyuan
user
yixuanyuan
X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
  Tomographic Reconstruction
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/.
X$^2$-Gaussian framework achieves high-fidelity 4D CT reconstruction through continuous-time modeling and self-supervised respiratory motion learning without external gating devices.
Gaussian splatting spatiotemporal encoder-decoder architecture Gaussian deformations physiology-driven periodic consistency loss differentiable optimization