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.