Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network

Abstract

Recently rapid and efficient localization of lung tumors based on single cone beam CT (CBCT) projection has attracted great interest in the radiation oncology community. High quality 4D-CBCT benefits accurate monitoring of moving targets during radiation therapy (RT). However, conventional iterative 4D-CBCT reconstruction process is heavily time-consuming. To address these issues, a motion sensitive cascade model was proposed. The proposed cascade model is composed of dual attention mechanism together with residual network (DA-ResNet) and a principal component analysis (PCA) model. It maps single projection from different breathing phase to each phase 3D-CBCT for achieving real time 4D-CBCT. The dual attention mechanism focuses on the motion information of both low-level features and high-level features to improve accuracy and efficiency of the network. The PCA model ensures a real time motion representation scheme. Compared with state-of-the-art networks, the proposed method outperformed them in the quantification labels of mean absolute error (MAE), R-squared (R2), normalized cross correlation (NCC), and structural similarity index measure (SSIM). This experiment was verified both on simulation and clinical data to support the conclusion.