Surrogate-Driven Motion Model for Motion Compensated Cone-Beam Ct Reconstruction Using Unsorted Projection Data

Cone-beam CT (CBCT) is widely used in image guided radiotherapy, but motion due to breathing can blur the image. Similar to 4DCT, 4D CBCT can reduce motion blur but 4D CBCT acquisitions take 2~4 times longer than 3D CBCT and often suffer from phase sorting artefact. This study aims to obtain motion models and motion-free images simultaneously from unsorted 3D CBCT projection data, using a general motion modelling framework previously proposed by our group, which was for the first time successfully applied to real CBCT data equivalent to a one-minute acquisition. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on real patient data. This study demonstrated the feasibility of our proposed framework for simultaneous motion model fitting and motion compensated reconstruction using unsorted 3D CBCT projection data.