4D CT imaging is an essential component of radiotherapy of thoracic/abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality. In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas. The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets. Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52%(INT) and 59% (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50% and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72% of the detectable artifacts were removed. The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information.

翻译：4D CT成像是对胸腹部肿瘤进行放疗的重要组成部分。然而，4D CT图像常受伪影影响，从而影响治疗计划的质量。本研究提出了一种基于深度学习的条件修补方法，用于恢复受伪影影响区域中解剖结构正确的图像信息。该复原方法包含两个阶段：首先基于深度学习检测常见的插值（INT）和双重结构（DS）伪影，随后对伪影区域应用条件修补技术。这里的“条件”是指利用患者特异性图像数据指导修补过程，以确保解剖结构的可靠性。研究基于65例肺癌患者的院内4D CT图像（其中48例仅含轻微伪影，17例伪影显著）以及两个公开可用的4D CT数据集（作为独立的外部测试集）。自动伪影检测结果显示，对INT和DS伪影的ROC-AUC分别达到0.99和0.97（院内数据）。所提出的修补方法使院内数据的均方根误差（RMSE）平均值分别降低52%（INT）和59%（DS）。在外部测试数据集中，RMSE改进幅度类似（分别为50%和59%）。应用于伪影显著的4D CT数据（非训练集样本）时，72%的可检测伪影被消除。这些结果凸显了基于深度学习的修补技术在恢复受伪影影响的4D CT数据方面的潜力。与近年来的4D CT修补及复原方法相比，本方法展示了利用患者特异性先验图像信息的优势。