Generation of a Mean Motion Model of the Lung Using 4D-CT Image Data

Modeling of respiratory motion gains in importance within the field of radiation therapy of lung cancer patients. Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient’s anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D CT data of different patients to extend motion modeling capabilities. Our modeling process consists of two main parts: an intra–subject registration to generate subject–specific motion models and an inter–subject registration to combine these subject–specific motion models into a mean motion model. Further, we present methods to adapt the mean motion model to a patient-specific lung geometry. A first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non–linear registration. Our results show that the average difference in prediction quality (measured by overlap coefficients) between non–linear registration and model–based prediction is approx. 10%. However, the patient–specific registration relies on individual 4D image data, whereas the model–based prediction was obtained without knowledge of the individual breathing dynamics. Results show that the model predicts motion patterns of individual patients generally well and we conclude from our results that such a model has the capability to provide valuable a-priori knowledge in many fields of applications.