A Physical Modeling Based Unification Framework for Cone Beam CT Reconstruction

Ti Bai, Xi Chen, Xuanqin Mou


Published in:Fully3D 2017 Proceedings


reconstruction, cone beam CT, noise, beam hardening, scatter
Data degradations including quantum noise, beam hardening and scatter remain a major issue in preclinical/clinical applications, despite the recent advances in x-ray computed tomography. Substantial efforts have been devoted to address individual degradations, however, little attention has been paid to minimize the adverse effects in a unified fashion. In this paper, we combine image reconstruction and artifact reduction in a physics-based synergistic framework. Simulation results showed that less than 10 HU error could be achieved with the proposed framework. Real data experiments showed that the corrected reconstructions with the proposed method exhibited comparable CT values and noise levels as the associated planning CT images.
Ti Bai
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
Xuanqin Mou
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
The 3D dictionary learning based sparse representation technique was adopted as the regularizer.

Correction results for the prostate patient case. (a) (c) correspond to the images of the planning CT, before correction and after correction, respectively. Three ROIs as indicated by the blue rectangles in the middle column are selected to calculate the CT values. Display window: [-250 250] HU.
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