A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.
10.12122/j.issn.1673-4254.2025.01.19
- Author:
Shizhou TANG
1
;
Ruolan SU
1
;
Shuting LI
1
;
Zhenzhen LAI
1
;
Jinhong HUANG
1
;
Shanzhou NIU
1
Author Information
1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.
- Publication Type:Journal Article
- Keywords:
anisotropic diffusion;
image reconstruction;
low-dose computed tomography;
sub-pixel
- MeSH:
Tomography, X-Ray Computed/methods*;
Algorithms;
Phantoms, Imaging;
Anisotropy;
Image Processing, Computer-Assisted/methods*;
Humans;
Radiation Dosage
- From:
Journal of Southern Medical University
2025;45(1):162-169
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion.
METHODS:The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data.
RESULTS:In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively.
CONCLUSIONS:The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.
