X²-高斯：面向连续时间断层重建的四维辐射高斯分布重建技术

摘要

四维计算机断层扫描（4D CT）重建对于捕捉动态解剖变化至关重要，但传统相位分选工作流程存在固有局限。现有方法通过呼吸门控设备将时间分辨率离散化为固定相位，导致运动错位并限制了临床实用性。本文提出X^2-Gaussian，一种创新框架，通过整合动态辐射高斯泼溅与自监督呼吸运动学习，实现连续时间4D-CT重建。我们的方法采用时空编码-解码架构预测时变高斯形变，消除了相位离散化。为摆脱对外部门控设备的依赖，我们引入了一种生理驱动的周期性一致性损失，通过可微分优化直接从投影中学习患者特异性呼吸周期。大量实验表明，该方法取得了最先进的性能，相较于传统方法提升了9.93 dB的峰值信噪比（PSNR），并较先前高斯泼溅技术提高了2.25 dB。通过将连续运动建模与无硬件周期学习相结合，X^2-Gaussian推动了动态临床成像中高保真4D CT重建的进步。项目网站：https://x2-gaussian.github.io/。

English

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/.

X²-高斯：面向连续时间断层重建的四维辐射高斯分布重建技术

X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction

摘要

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