X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
  Tomographic Reconstruction
Authors: name: Weihao Yu; name: Yuanhao Cai; name: Ruyi Zha; name: Zhiwen Fan; name: Chenxin Li; name: Yixuan Yuan
Published: 2025-03-27 17:59:57

Abstract: 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/.