In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patients breathing cycle prior to beam delivery. However, generating 4D-CBCT images with sufficient quality requires significantly more projection images than a standard 3D-CBCT scan, leading to extended scanning times and increased imaging dose to the patient. To address these limitations, there is a strong demand for methods capable of reconstructing high-quality 4D-CBCT images from a 1-minute 3D-CBCT acquisition. The challenge lies in the sparse sampling of projections, which introduces severe streaking artifacts and compromises image quality. This paper introduces a novel framework leveraging spatiotemporal Gaussian representation for 4D-CBCT reconstruction from sparse projections, achieving a balance between streak artifact reduction, dynamic motion preservation, and fine detail restoration. Each Gaussian is characterized by its 3D position, covariance, rotation, and density. Two-dimensional X-ray projection images can be rendered from the Gaussian point cloud representation via X-ray rasterization. The properties of each Gaussian were optimized by minimizing the discrepancy between the measured projections and the rendered X-ray projections. A Gaussian deformation network is jointly optimized to deform these Gaussian properties to obtain a 4D Gaussian representation for dynamic CBCT scene modeling. The final 4D-CBCT images are reconstructed by voxelizing the 4D Gaussians, achieving a high-quality representation that preserves both motion dynamics and spatial detail.
在图像引导放射治疗（Image-Guided Radiotherapy, IGRT）中，四维锥束计算机断层扫描（4D-CBCT）对于在放射束投放前评估患者呼吸周期中的肿瘤运动至关重要。然而，生成具有足够质量的 4D-CBCT 图像需要显著多于标准 3D-CBCT 扫描的投影图像，导致扫描时间延长和患者受照剂量增加。为解决这些限制，迫切需要能够从 1 分钟的 3D-CBCT 采集中重建高质量 4D-CBCT 图像的方法。挑战在于投影采样稀疏性，这会引入严重的条纹伪影并降低图像质量。 本文提出了一种新颖框架，利用时空高斯表示（spatiotemporal Gaussian representation）从稀疏投影中重建 4D-CBCT 图像，在条纹伪影减少、动态运动保留和细节恢复之间实现平衡。每个高斯由其三维位置、协方差、旋转和密度表征。通过 X 射线光栅化，可以从高斯点云表示渲染二维 X 射线投影图像。通过最小化测量投影与渲染投影之间的差异，优化每个高斯的属性。 框架中还联合优化了一个高斯变形网络，用于变形这些高斯属性，从而获得动态 CBCT 场景建模的 4D 高斯表示。最终的 4D-CBCT 图像通过对 4D 高斯进行体素化重建，生成了高质量的图像表示，既保留了运动动态，又保持了空间细节。这种方法为 4D-CBCT 重建提供了一种高效且精准的解决方案。