Robust CBCT Reconstruction Based On Low-Rank Tensor Decomposition And Total Variation Regularization

摘要

Cone-beam computerized tomography (CBCT) has been widely used in numerous clinical applications. To reduce the effects of X-ray on patients, a low radiation dose is always recommended in CBCT. However, noise will seriously degrade image quality under a low dose condition because the intensity of the signal is relatively low. In this study, we propose to use the Huber loss function as a data fidelity term in CBCT reconstruction, making the reconstruction robust to impulse noise under low radiation dose condition. Furthermore, a low-rank tensor property is adopted as the prior term. Such property is helpful in recovering the missing structure information caused by impulse noise. The proposed CBCT reconstruction model is formulated by further integrating a 3D total variation term for reducing Gaussian noise. An alternative direction multiplier method is adopted to solve the optimization problem. Experiments on simulated and real data show that the proposed model outperforms existing CBCT reconstruction algorithms.