5D respiratory motion model based image reconstruction algorithm for 4D cone-beam computed tomography

摘要

4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.