Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy

International audienceThe aim of this study is the development and experimental testing of a tumor tracking method for particle radiation therapy, providing the daily respiratory dynamics of the patient's thoraco-abdominal anatomy as a function of an external surface surrogate combined with an a priori motion model. The proposed tracking approach is based on a patient-specific breathing motion model, estimated from the four-dimensional (4D) planning computed tomography (CT) through deformable image registration. The model is adapted to the interfraction baseline variations in the patient's anatomical configuration. The driving amplitude and phase parameters are obtained intrafractionally from a respiratory surrogate signal derived from the external surface displacement. The developed technique was assessed on a dataset of seven lung cancer patients, who underwent two repeated 4D CT scans. The first 4D CT was used to build the respiratory motion model, which was tested on the second scan. The geometric accuracy in localizing lung lesions, mediated over all breathing phases, ranged between 0.6 and 1.7 mm across all patients. Errors in tracking the surrounding organs at risk, such as lungs, trachea and esophagus, were lower than 1.3 mm on average. The median absolute variation in water equivalent path length (WEL) within the target volume did not exceed 1.9 mm-WEL for simulated particle beams. A significant improvement was achieved compared with error compensation based on standard rigid alignment. The present work can be regarded as a feasibility study for the potential extension of tumor tracking techniques in particle treatments. Differently from current tracking methods applied in conventional radiotherapy, the proposed approach allows for the dynamic localization of all anatomical structures scanned in the planning CT, thus providing complete information on density and WEL variations required for particle beam range adaptation