In this study, we further lowered the projection number used for undersampled CBCT reconstruction. Thirty-six (10%) projections were extracted from the 360-projection dataset to reconstruct the undersampled CBCT. Both the undersampled DRR data reconstructed from the 36- and 72-projection data were used to compare the effects of projection numbers on transfer learning. We chose the better method of the two transfer learning methods to evaluate this effect. The two models were trained using the basic U-Net model with 36- and 72-projection data, respectively. Starting with the two models, the 1st- and 2nd-day’s patient data were used as training data for transfer learning. The results for both the basic U-Net model and transfer learning model were evaluated using the planning CT image as a reference to determine the difference between the improvements from the transfer learning methods for CBCT images reconstructed with different numbers of projections.

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