(Monday, 7/15/2019) 7:30 AM - 9:30 AM
Room: 221AB
Purpose: Previously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, deformable registration, which used to deform the edge from planning CT to on-board volumes, is time-consuming and user dependent. This study aims to compare a supervised and an unsupervised learning model for predicting daily on-board edge deformation to bypass deformable registration to improve the PCTV reconstruction efficiency.
Methods: The new method used supervised/unsupervised CNN deformation prediction and PCTV reconstruction. For the supervised model, deformation vector field (DVF) registered from CT to full-sampled CBCTs was used as the ground-truth to train the model to register CT and retrospectively under-sampled low-dose CBCT on the first day. The model was then updated with the following days’ data. For the unsupervised model, only CT and CBCT image pairs were used as input to train the model with no ground-truth DVF needed. The model was initially trained on a large amount of MRI data, and then was fine-tuned on our lung patient data. Both supervised and unsupervised learning model were evaluated using lung SBRT patient data. The first n-1 day’s CBCTs were used for model training to predict nᵗʰ day edge information (n=2, 3, 4, 5). 45 half-fan projections covering 360˚ from nth day CBCT were used for reconstruction. Results from Edge-preserving total variation (EPTV), PCTV and PCTV-CNN were compared.
Results: The cross correlation between the predicted edge map and the reference is around 0.74 and 0.87, for supervised and unsupervised model respectively. PCTV-CNN with the unsupervised model was robust to the daily changes.
Conclusion: Preliminary results demonstrated the feasibility to use the unsupervised CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. The accuracy of the supervised CNN might be affected by the limited data size and inaccurate ground truth DVF.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by NIH grant R01 CA-184173.
Keywords
Image-guided Therapy, Reconstruction
Taxonomy
IM- Cone Beam CT: Image Reconstruction
Contact Email
