4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

中文翻译：

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结合运动估计、运动补偿重建、生物力学建模和深度学习的先进 4 维锥形束计算机断层扫描重建


4 维锥形束计算机断层扫描 (4D-CBCT) 在移动目标定位/描绘、结构去模糊、目标运动跟踪、治疗剂量积累和自适应放射治疗方面比传统 3D-CBCT 具有几个关键优势。然而，4D-CBCT 在当前放射治疗实践中的使用受到限制，主要是由于锥束投影的有限角度采样导致其图像质量不佳。在本研究中，我们总结了用于提高图像质量的4D-CBCT重建技术的最新进展，并介绍了我们开发的一种新的4D-CBCT重建技术，该技术具有同时运动估计和图像重建（SMEIR）的特点。在原有SMEIR方案的基础上，引入生物力学建模引导的SMEIR（SMEIR-Bio），进一步提高肺部4D-CBCT精细细节的重建精度。为了提高重建效率，我们最近开发了一种基于U-net的变形矢量场（DVF）优化技术，利用基于群体的深度学习方案来提高肺内DVF的准确性（SMEIR-Unet），没有明确的生物力学建模。本研究中包含了 SMEIR、SMEIR-Bio 和 SMEIR-Unet 技术的详细信息，以及比较 CBCT 图像和 DVF 重建精度的相应结果。我们还讨论了SMEIR型技术在图像引导放射治疗和适应性放射治疗中的应用前景，并提出了未来发展的潜在方案，以实现更快、更准确的4D-CBCT成像。