Affiliations: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Kuidong Huang and Fuqiang Yang, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China. E-mail: kdhuang@nwpu.edu.cn (K. Huang); E-mail: fqyang@nwpu.edu.cn (F. Yang)
Abstract: Based on the structural tensor of projection, this study aims to address and test a new improved algorithm applying to the distort projection data to generate a high qualified image by reducing the artifacts and noise from scattering in the cone-beam computed tomography (CBCT). Since the scattering information has a large relationship with the structure of the object, which is reflected by the projection, regional model knowledge for scattering is accomplished by finding the relationship between projection and scattering. As the tensor, the gradient of projection is first calculated in the process for estimating the direction and structural edge of the object. Then, the Determinant and Traces of the tensor map with different characteristics are computed to determine the different regions. By modeling and fitting the regions of scattering distribution, the knowledge of scattering parameters corresponding to a different region is obtained. Based on the similarity of scattering distribution in adjacent angles, the scatterings with angle sequence are completed by interpolating the prior knowledge obtained through the sparse sampling. By performing the studies on polychromatic X-ray to test the performance of the scattering estimation algorithm, the results show a significant improvement in the images that are reconstructed from the corrected projection. The root mean square error (RMSE) of the proposed method is reduced by 21.8% and 39.8%, respectively. Peak signal to noise ratio (PSNR), and universal quality index (UQI) also indicate better uniformity, where the PSNR is increased by 7.4% and 56.7%, UQI is increased by 70.8% and 262.3% for experimental #Wheel and #Cylinder, respectively.
