Prediction of Lung Motion From Four-Dimensional Computer Tomography (4DCT) Images Using Bayesian Registration and Trajectory Modelling

Respiratory motion causes difficulty in locating tumours in the thorax and upper abdomen for image-guided radiotherapy. Precisely predicting the respiratory-induced organ motion is still a challenging problem at present. In this paper, to predict the motion of lungs in a respiratory cycle, we propose a novel method comprising Bayesian registration and trajectory modelling based on cine four-dimensional computer tomography (4DCT) images. Specifically, we take the CT image captured at the end-inhale phase as the source image and those captured at other phases as the moving images. We then align the source image to each moving-phase image to generate the displacement fields using the Bayesian registration method. The lung-motion trajectory is then modelled based on a continuous time-related displacement field by linking the displacement fields at discrete phases. The results indicate that any point in the lungs at any given time is accurately predicted using the proposed method, which provides an alternative method of estimating the lung and tumour motions for radiation therapy.