Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The
mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose.
The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT
image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray
tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image
reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior
information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise
suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares
(PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed
LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less
diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region
around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the
sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution
tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in
edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.