Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Effective and accurate respiration estimation is crucial to account for the motion effects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause difficulties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere. Learning-based linear motion modeling techniques have the advantage of incorporating prior knowledge for robust motion estimation. In this research shape-correlation deformation statistics (SCDS) capture strong correlations between the shape of the lung and the dense deformation field under breathing. Dimension reduction and linear regression techniques are used to extract the correlation statistics. Based on the assumption that the deformation correlations are consistent between planning and treatment time, patient-specific SCDS trained from a 4D planning image sequence is used to predict the respiratory motion in the patient's artifact-laden 4D treatment image sequence. Furthermore, a prediction-driven atlas formation method is developed to weaken the consistency assumption, by integrating intensity information from the target images and the SCDS predictions into a common optimization framework. The strategy of balancing between the prediction constraints and the intensity-matching forces makes the method less sensitive to variation in the correlation and utilizes intensity information besides the lung boundaries. This strategy thus provides improved motion estimation accuracy and robustness. The SCDS-based methods are shown to be effective in modeling and estimating respiratory motion in lung, with evaluations and comparisons carried out on both simulated images and patient images
