Four‐dimensional cone‐beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due to severe sparse view sampling artifacts, current 4D CBCT data lack sufficient image quality for accurate motion quantification. In the present paper, we introduce a deep learning‐based framework for boosting the image quality of 4D CBCT image data that can be combined with any CBCT reconstruction approach and clinical 4D CBCT workflow.

中文翻译：

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独立的基于深度学习的4D锥束CT重建增强

有人提出采用四锥束计算机断层扫描（4D CBCT）成像作为解决运动目标在放射治疗（RT）过程中运动目标（例如肺和肝）的分数运动变异性的解决方案。但是，由于严重的稀疏视图采样伪像，当前的4D CBCT数据缺乏足够的图像质量，无法进行精确的运动量化。在本文中，我们引入了一个基于深度学习的框架来提高4D CBCT图像数据的图像质量，可以将其与任何CBCT重建方法和临床4D CBCT工作流程结合使用。