论文标题
基于深度学习的有条件介绍以恢复受伪影的4D CT图像
Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images
论文作者
论文摘要
4D CT成像是胸部/腹部肿瘤放疗的重要组成部分。但是，4D CT图像通常受到损害治疗计划质量的工件的影响。在这项工作中，提出了基于深度学习（DL）的条件涂料，以恢复受伪影影响区域的解剖学上正确的图像信息。恢复方法包括一个两阶段的过程：基于DL的常见插值（INT）和双重结构（DS）伪影的检测，然后是适用于工件区域的条件涂层。在这种情况下，有条件地是指通过特定于患者的图像数据对介绍过程的指导，以确保解剖上可靠的结果。该研究基于65个内部4D CT肺癌患者的图像（48例只有微小的伪影，17个带有明显的伪像）和两个公开可用的4D CT数据集，可作为独立的外部测试集。自动伪影检测显示，INT的ROC-AUC为0.99，DS伪像（内部数据）为0.97。所提出的涂料方法将内部数据的平均根平方误差（RMSE）降低了52％（int），而内部数据的平均均方根误差（RMSE）和59％（ds）。对于外部测试数据集，RMSE的改进相似（分别为50％和59％）。使用明显的伪影（不是训练集的一部分）应用于4D CT数据，删除了72％的可检测伪影。结果突出了基于DL基于伪像的4D CT数据恢复基于DL的潜在。与最近的4D CT介入和恢复方法相比，提出的方法说明了利用特定于患者的先验图像信息的优势。
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.
