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

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.

[1]  Li Zhang,et al.  A comparison of projection domain noise reduction methods in low-dose dental CBCT , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[2]  Jing Wang,et al.  A Biomechanical Modeling Guided CBCT Estimation Technique , 2017, IEEE Transactions on Medical Imaging.

[3]  Shutao Li,et al.  Tensor Completion via Nonlocal Low-Rank Regularization , 2019, IEEE Transactions on Cybernetics.

[4]  M. Vannier,et al.  Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? , 2009, Inverse problems.

[5]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[6]  Xiaochuan Pan,et al.  Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy , 2015, Physics in medicine and biology.

[7]  Ming Jiang,et al.  Convergence of the simultaneous algebraic reconstruction technique (SART) , 2003, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[8]  Gangyao Kuang,et al.  Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Xin Tian.  Multiscale Sparse Dictionary Learning With Rate Constraint for Seismic Data Compression , 2019, IEEE Access.

[10]  Nikos D. Sidiropoulos,et al.  Convergence of the Huber Regression M-Estimate in the Presence of Dense Outliers , 2014, IEEE Signal Processing Letters.

[11]  Hsuan-Ming Huang,et al.  Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization , 2016, PloS one.

[12]  Wei Chen,et al.  Robust single-photon counting imaging with spatially correlated and total variation constraints. , 2020, Optics express.

[13]  Junjun Jiang,et al.  FusionGAN: A generative adversarial network for infrared and visible image fusion , 2019, Inf. Fusion.

[14]  L. Xing,et al.  Iterative image reconstruction for CBCT using edge-preserving prior. , 2008, Medical physics.

[15]  Jiayi Ma,et al.  Infrared and visible image fusion via gradient transfer and total variation minimization , 2016, Inf. Fusion.

[16]  Stefanos Zafeiriou,et al.  Robust low-rank tensor modelling using Tucker and CP decomposition , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[17]  William Y. Song,et al.  Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery , 2017, Physics in medicine and biology.

[18]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.