Purpose: Radiomics features extracted from 4D-CBCT provide valuable information for outcome prediction throughout the treatment courses. However, due to the time and dose constraint, 4D-CBCT images suffer from severe streak artifacts caused by under-sampling, which can affect the accuracy of radiomic features. This study aims to investigate patient-specific models to optimize the 4D-CBCT image quality for individual patients for radiomic analysis.
Methods: Different from group-based models, patient-specific models use training data from only one patient to optimize the model. In this study, patients with multiple 4D-CT scans were accrued. 4D-CBCT was simulated from the first 4D-CT by FDK algorithm and then augmented with translation, rotation, and deformation. This augmented 4D-CBCT dataset was used to train a patient-specific model developed based on Pix2pix network to enhance 4D-CBCT to match with ground-truth 4D-CT. The model was then tested to enhance 4D-CBCT simulated from the second 4D-CT. The accuracy of the enhanced 4D-CBCT was evaluated by comparing its radiomic features with ground-truth 4D-CT features. The impact of model dimensionality, region of interest (ROI) selection, and loss function were also investigated by modifying the structure and loss functions of the model.
Results: Patient-specific models with different dimensionality achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone. For example, the LLL max feature improved 44.5% on average by replacing ROI loss with WB+ROI loss. Overall, patient-specific training models could further improve the accuracy of radiomic features compared to the group-based model.
Conclusion: This study demonstrated the efficacy of using patient-specific deep learning models to enhance 4D-CBCT to improve its radiomic feature accuracy. ROI selection can impact the accuracy of the enhancement. Patient-specific model opens a new horizon for customized image enhancement to achieve high precision radiomic analysis.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the National Institutes of Health under Grant No. R01-CA184173 and R01- EB028324.
Cone-beam CT, Quantitative Imaging