LAUSR

Accurate localization of lung tumor in real time based on a single X-ray projection is of great interest to the tumor-tracking radiotherapy but is very challenging. In this paper, a convolutional neural network (CNN)-based tumor localization method was proposed to address this problem with the aid of principal component analysis-based motion modeling. A CNN regression model was trained before treatment to recover the ill-conditioned nonlinear mapping from the single X-ray projection to the tumor motion. Novel intensity correction and data augmentation techniques were adopted to improve the model’s robustness to the scatter and noise in the X-ray projection image. During treatment, the volumetric image and tumor position could be obtained by applying the CNN model on the acquired X-ray projection. This method was validated and compared with the other state-of-the-art methods on three real patient data. It was found that the proposed method could achieve real-time tumor localization with much higher accuracy (<1 mm) and robustness.