To achieve better performance for 4D multi-frame reconstruction with the parametric motion model (MF-PMM), a general simultaneous motion estimation and image reconstruction (G-SMEIR) method is proposed. In G-SMEIR, projection domain motion estimation and image domain motion estimation are performed alternatively to achieve better 4D reconstruction. This method can mitigate the local optimum trapping problem in either domain. To improve computational efficiency, the image domain motion estimation is accelerated by adapting fast convergent algorithms and graphics processing unit (GPU) computing. The proposed G-SMEIR method is tested using a cone-beam computed tomography (CBCT) simulation study of 4D XCAT phantom at different dose levels and compared with 3D total variation-based reconstruction (3D TV), 4D reconstruction with image domain motion estimation (IM4D), and SMEIR. G-SMEIR shows strong denoising capability and achieves similar performance at regular dose and half dose. The root mean squared error (RMSE) of G-SMEIR is the best among the four methods and improved about 12% over SMEIR for all respiratory phase images at full dose. G-SMEIR also achieved the best structural similarity index (SSIM) values among all methods. More importantly, G-SMEIR leads to more than 40% improvement of the mean deviation from the phantom tumor motion over SMEIR. A preliminary patient CBCT image reconstruction also shows better image quality of G-SMEIR than that of the frame-by-frame reconstruction (3D TV) and MF-PMM either using image domain motion estimation (IM4D) or using projection domain motion estimation (SMEIR) alone. G-SMEIR with a flexible combination of image domain and projection domain motion estimation provides an effective tool for 4D tomographic reconstruction.

为了使用参数运动模型 (MF-PMM) 实现更好的 4D 多帧重建性能，提出了一种通用的同时运动估计和图像重建 (G-SMEIR) 方法。在G-SMEIR中，投影域运动估计和图像域运动估计交替进行，以实现更好的4D重建。这种方法可以减轻任一域中的局部最优陷阱问题。为了提高计算效率，通过采用快速收敛算法和图形处理单元 (GPU) 计算来加速图像域运动估计。所提出的 G-SMEIR 方法使用锥形束计算机断层扫描 (CBCT) 模拟研究对不同剂量水平的 4D XCAT 模型进行测试，并与基于全变的 3D 重建 (3D TV) 进行比较，使用图像域运动估计 (IM4D) 和 SMEIR 进行 4D 重建。G-SMEIR 显示出很强的去噪能力，并且在常规剂量和半剂量下实现了相似的性能。G-SMEIR 的均方根误差 (RMSE) 是四种方法中最好的，在全剂量下所有呼吸相图像的均方根误差 (RMSE) 比 SMEIR 提高了约 12%。G-SMEIR 还取得了所有方法中最好的结构相似性指数 (SSIM) 值。更重要的是，与 SMEIR 相比，G-SMEIR 导致幻影肿瘤运动的平均偏差提高了 40% 以上。初步的患者 CBCT 图像重建也显示 G-SMEIR 的图像质量优于逐帧重建 (3D TV) 和使用图像域运动估计 (IM4D) 或使用投影域运动估计 (SMEIR) 的 MF-PMM ） 独自的。