Madesta Frederic, Sentker Thilo, Gauer Tobias, Werner René
Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany.
Department of Radiotherapy and Radio-Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany.
Med Phys. 2020 Nov;47(11):5619-5631. doi: 10.1002/mp.14441. Epub 2020 Oct 15.
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.
Boosting is achieved by learning the relationship between so-called sparse view pseudo-time-average CBCT images obtained by a projection selection scheme introduced to mimic phase image sparse view artifact characteristics and corresponding time-average CBCT images obtained by full view reconstruction. The employed convolutional neural network architecture is the residual dense network (RDN). The underlying hypothesis is that the RDN learns the appearance of the streaking artifacts that is typical for 4D CBCT phase images - and removes them without influencing the anatomical image information. After training the RDN, it can be applied to the 4D CBCT phase images to enhance the image quality without affecting the contained temporal and motion information. Different to existing approaches, no patient-specific prior knowledge about anatomy or motion characteristics is needed, that is, the proposed approach is self-contained.
Application of the trained network to reconstructed phase images of an external (SPARE challenge) as well as in-house 4D CBCT patient and motion phantom data set reduces the phase image streak artifacts consistently for all patients and state-of-the-art reconstruction approaches. Using the SPARE data set, we show that the root mean squared error compared to ground truth data provided by the challenge is reduced by approximately 50% while normalized cross correlation of reconstruction and ground truth is improved up to 10%. Compared to direct deep learning-based 4D CBCT to 4D CT mapping, our proposed method performs better because inappropriate prior knowledge about the patient anatomy and physiology is taken into account. Moreover, the image quality enhancement leads to more plausible motion fields estimated by deformable image registration (DIR) in the 4D CBCT image sequences.
The presented framework enables significantly boosting of 4D CBCT image quality as well as improved DIR and motion field consistency. Thus, the proposed method facilitates extraction of motion information from severely artifact-affected images, which is one of the key challenges of integrating 4D CBCT imaging into RT workflows.
四维锥形束计算机断层扫描（4D CBCT）成像被认为是一种解决放疗（RT）过程中肺部和肝脏等移动靶区分次间运动变化的方法。然而，由于严重的稀疏视图采样伪影，当前的4D CBCT数据缺乏足够的图像质量进行精确的运动量化。在本文中，我们介绍了一种基于深度学习的框架，用于提高4D CBCT图像数据的图像质量，该框架可与任何CBCT重建方法和临床4D CBCT工作流程相结合。
通过学习通过引入投影选择方案获得的所谓稀疏视图伪时间平均CBCT图像（用于模拟相位图像稀疏视图伪影特征）与通过全视图重建获得的相应时间平均CBCT图像之间的关系来实现增强。所采用的卷积神经网络架构是残差密集网络（RDN）。基本假设是RDN学习4D CBCT相位图像典型的条纹伪影外观，并在不影响解剖图像信息的情况下消除它们。在训练RDN之后，可以将其应用于4D CBCT相位图像以提高图像质量，同时不影响所包含的时间和运动信息。与现有方法不同，不需要关于解剖结构或运动特征的患者特定先验知识，即所提出的方法是自包含的。
将训练好的网络应用于外部（SPARE挑战）以及内部4D CBCT患者和运动体模数据集的重建相位图像，对于所有患者和最先进的重建方法，都能一致地减少相位图像条纹伪影。使用SPARE数据集，我们表明与挑战提供的真实数据相比，均方根误差降低了约50%，而重建与真实数据的归一化互相关提高了10%。与基于深度学习的直接4D CBCT到4D CT映射相比，我们提出的方法表现更好，因为考虑了关于患者解剖结构和生理的不适当先验知识。此外，图像质量的提高导致在4D CBCT图像序列中通过可变形图像配准（DIR）估计的运动场更合理。
所提出的框架能够显著提高4D CBCT图像质量以及改善DIR和运动场一致性。因此，所提出的方法有助于从受伪影严重影响的图像中提取运动信息，这是将4D CBCT成像集成到RT工作流程中的关键挑战之一。
