Conventional in-room cone-beam computed tomography (CBCT) lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and/or adaptive treatment planning. In-room four-dimensional (4D) CBCT image acquisition is still time consuming and suffers the same issue of poor image quality. To overcome this limitation, we developed a computational framework to digitally synthesize high-quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset. A patient-specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases. Subsequently, the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration. The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient's anatomy at the time of treatment. We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in-room CBCT setup and weekly 4D CT for treatment evaluation. Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans. Various imaging artifacts were also suppressed and soft-tissue visibility was enhanced. 
Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion