Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN).

purposePreviously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, the iterative deformable registration method used for deforming edges from planning-CT to on-board CBCT is time-consuming and user-dependent. This study aims to automate and accelerate PCTV reconstruction by developing an unsupervised CNN model to bypass the conventional deformable registration.

methodsThe new method uses unsupervised CNN model for deformation prediction and PCTV reconstruction. An unsupervised CNN model with a u-net structure was used to predict deformation vector fields (DVF) to generate on-board contours for PCTV reconstruction. Paired 3D image volumes of prior CT and on-board CBCT are inputs and DVF are predicted without the need of ground truths. The model was initially trained on brain MRI images, and then fine-tuned using our lung SBRT data. This method was evaluated using lung SBRT patient data. In the intra-patient study, the first

resultsThe cross-correlations of the predicted edge map and the ground truth were on average 0.88 for both intra-patient and inter-patient studies. PCTV-CNN achieved comparable image quality as PCTV while automating the registration process and reducing the registration time from 1-2 min to 1.4 s.

conclusionIt is feasible to use an unsupervised CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low-dose CBCT to improve the precision of on-board target localization and adaptive radiotherapy.