Adaptive orthogonal directional total variation with kernel regression for CT image denoising

Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images.

In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression.

The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation.

Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance.

The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.