Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images

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. Evaluation is based on 65 in-house 4D CT data sets of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and the publicly available DIRLab 4D CT data (independent external test set). Automated artifact detection revealed a ROC-AUC of 0.99 for INT and 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 60% (DS) and 42% (INT) for the in-house evaluation data (simulated artifacts for the slight artifact data; original data were considered as ground truth for RMSE computation). For the external DIR-Lab data, the RMSE decreased by 65% and 36%, respectively. Applied to the pronounced artifact data group, on average 68% of the detectable artifacts were removed. The results highlight the potential of DL-based inpainting for the restoration of artifact-affected 4D CT data. Improved performance of conditional inpainting (compared to standard inpainting) illustrates the benefits of exploiting patient-specific prior knowledge.