Fifty-eighth annual meeting of the american association of physicists in medicine
TH-EF-BRA-01: 5D Respiratory Motion Model Based Iterative Reconstruction Method for 4DCBCT
The 4DCBCT image reconstruction suffers from the insufficient number of projections and the error from projection binning. We have recently developed a 5D respiratory motion model based iterative reconstruction method (5D method) for 4DCBCT in a proof-of-concept 2D setting. The 5D method reconstructs a reference image and two time-independent vector fields without projection binning, based on which any temporal phase at each projection can be generated. As a result, the 5D method needs fewer number of projections and is free from binning error. This work is to develop a 3D version of the 5D method.
The 5D method is based on the 5D respiratory motion model, for which the tidal volume and airflow are measured together with projection data. To reconstruct a reference image and two time-independent vector fields that are needed to generate any temporal phase for each projection, the 5D method is formulated as a optimization problem with total variation (TV) regularization on both reference image and vector fields. 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 proposed 5D method was validated in simulation based on measurements of a lung patient, in comparison with the state-of-art spatiotemporal-TV-based 4DCBCT iterative method. And the results suggest that the 5D method had significantly improved reconstruction image quality and reduced quantitative error.
A novel iterative image reconstruction method for 4DCBCT based on 5D respiratory motion model has been developed with no projection binning requirement and improved reconstruction image quality.
Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).