  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.
