标题： 时空高斯优化用于从稀疏投影中重建4D锥束CT 显示英文标题

标题： Spatiotemporal Gaussian Optimization for 4D Cone Beam CT Reconstruction from Sparse Projections

摘要： 在图像引导放疗（IGRT）中，四维锥束计算机断层扫描（4D-CBCT）对于在束流输送前评估患者呼吸周期中的肿瘤运动至关重要。 然而，生成足够质量的4D-CBCT图像需要比标准3D-CBCT扫描更多的投影图像，导致扫描时间延长和患者成像剂量增加。 为了解决这些限制，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。 挑战在于投影的稀疏采样，这会引入严重的条纹伪影并影响图像质量。 本文介绍了一种新颖的框架，利用时空高斯表示从稀疏投影中进行4D-CBCT重建，实现了条纹伪影减少、动态运动保留和精细细节恢复之间的平衡。 每个高斯分布由其三维位置、协方差、旋转和密度来表征。 可以通过X射线光栅化从高斯点云表示中渲染二维X射线投影图像。 通过最小化测量投影与渲染X射线投影之间的差异来优化每个高斯的属性。 一个高斯变形网络被联合优化，以变形这些高斯属性，从而获得用于动态CBCT场景建模的4D高斯表示。 最终的4D-CBCT图像通过将4D高斯体素化来重建，实现了既保留运动动态又保留空间细节的高质量表示。 代码和重建结果可在https://github.com/fuyabo/4DGS_for_4DCBCT/tree/main找到。 显示英文摘要

摘要： 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. The code and reconstruction results can be found at https://github.com/fuyabo/4DGS_for_4DCBCT/tree/main