Development and clinical evaluation of a three-dimensional cone-beam computed tomography estimation method using a deformation field map

Purpose: To develop a three-dimensional (3D) cone-beam computed tomography (CBCT) estimation method using a deformation field map, and to evaluate and optimize the efficiency and accuracy of the method for use in the clinical setting. Methods and Materials: We propose a method to estimate patient CBCT images using prior information and a deformation model. Patients' previous CBCT data are used as the prior information, and the new CBCT volume to be estimated is considered as a deformation of the prior image volume. The deformation field map is solved by minimizing deformation energy and maintaining new projection data fidelity using a nonlinear conjugate gradient method. This method was implemented in 3D form using hardware acceleration and multi-resolution scheme, and it was evaluated for different scan angles, projection numbers, and scan directions using liver, lung, and prostate cancer patient data. The accuracy of the estimation was evaluated by comparing the organ volume difference and the similarity between estimated CBCT and the CBCT reconstructed from fully sampled projections. Results: Results showed that scan direction and number of projections do not have significant effects on the CBCT estimation accuracy. The total scan angle is the dominant factor affecting the accuracy of the CBCT estimation algorithm. Larger scan angles yield better estimation accuracy than smaller scan angles. Lung cancer patient data showed that the estimation error of the 3D lung tumor volume was reduced from 13.3% to 4.3% when the scan angle was increased from 60° to 360° using 57 projections. Conclusions: The proposed estimation method is applicable for 3D DTS, 3D CBCT, four-dimensional CBCT, and four-dimensional DTS image estimation. This method has the potential for significantly reducing the imaging dose and improving the image quality by removing the organ distortion artifacts and streak artifacts shown in images reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm.