Due to a limited number of projections at each phase, severe view aliasing artifacts are present in four-dimensional cone beam computed tomography (4D-CBCT) when reconstruction is performed using conventional algorithms. In this work, we aim to obtain high-quality 4D-CBCT of lung cancer patients in radiation therapy by deforming the planning CT. The deformation vector fields (DVF) to deform the planning CT are estimated through matching the forward projection of the deformed prior image and measured on-treatment CBCT projection. The estimation of the DVF is formulated as an unconstrained optimization problem, where the objective function to be minimized is the sum of the squared difference between the forward projection of the deformed planning CT and the measured 4D-CBCT projection. A nonlinear conjugate gradient method is used to solve the DVF. As the number of the variables in the DVF is much greater than the number of measurements, the solution to such a highly ill-posed problem is very sensitive to the initials during the optimization process. To improve the estimation accuracy of DVF, we proposed a new strategy to obtain better initials for the optimization. In this strategy, 4D-CBCT is first reconstructed by total variation minimization. Demons deformable registration is performed to register the planning CT and the 4D-CBCT reconstructed by total variation minimization. The resulted DVF from demons registration is then used as the initial parameters in the optimization process. A 4D nonuniform rotational B-spline-based cardiac-torso (NCAT) phantom and a patient 4D-CBCT are used to evaluate the algorithm. Image quality of 4D-CBCT is substantially improved by using the proposed strategy in both NCAT phantom and patient studies. The proposed method has the potential to improve the temporal resolution of 4D-CBCT. Improved 4D-CBCT can better characterize the motion of lung tumors and will be a valuable tool for image-guided adaptive radiation therapy.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Radiological and Ultrasound Technology