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.
Tomography, X-Ray Computed/methods* ; Algorithms ; Phantoms, Imaging ; Anisotropy ; Image Processing, Computer-Assisted/methods* ; Humans ; Radiation Dosage

Objective We propose a low-dose CT reconstruction method using partial differential equation (PDE) denoising under high-dimensional constraints. Methods The projection data were mapped into a high-dimensional space to construct a high-dimensional representation of the data, which were updated by moving the points in the high-dimensional space. The data were denoised using partial differential equations and the CT image was reconstructed using the FBP algorithm. Results Compared with those by FBP, PWLS-QM and TGV-WLS methods, the relative root mean square error of the Shepp-Logan image reconstructed by the proposed method were reduced by 68.87％, 50.15％ and 27.36％, the structural similarity values were increased by 23.50％, 8.83％ and 1.62％, and the feature similarity values were increased by 17.30％, 2.71％ and 2.82％, respectively. For clinical image reconstruction, the proposed method, as compared with FBP, PWLS-QM and TGV-WLS methods, resulted in reduction of the relative root mean square error by 42.09％, 31.04％and 21.93％, increased the structural similarity values by 18.33％, 13.45％ and 4.63％, and increased the feature similarity values by 3.13％, 1.46％ and 1.10％, respectively. Conclusion The new method can effectively reduce the streak artifacts and noises while maintaining the spatial resolution in reconstructed low-dose CT images.

Objective We propose a low-dose CT reconstruction method using partial differential equation (PDE) denoising under high-dimensional constraints. Methods The projection data were mapped into a high-dimensional space to construct a high-dimensional representation of the data, which were updated by moving the points in the high-dimensional space. The data were denoised using partial differential equations and the CT image was reconstructed using the FBP algorithm. Results Compared with those by FBP, PWLS-QM and TGV-WLS methods, the relative root mean square error of the Shepp-Logan image reconstructed by the proposed method were reduced by 68.87％, 50.15％ and 27.36％, the structural similarity values were increased by 23.50％, 8.83％ and 1.62％, and the feature similarity values were increased by 17.30％, 2.71％ and 2.82％, respectively. For clinical image reconstruction, the proposed method, as compared with FBP, PWLS-QM and TGV-WLS methods, resulted in reduction of the relative root mean square error by 42.09％, 31.04％and 21.93％, increased the structural similarity values by 18.33％, 13.45％ and 4.63％, and increased the feature similarity values by 3.13％, 1.46％ and 1.10％, respectively. Conclusion The new method can effectively reduce the streak artifacts and noises while maintaining the spatial resolution in reconstructed low-dose CT images.