![]() Methods: In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling.
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