A pre-log correction method based on dynamic approximation to reduce photon-starved deterioration.

Photon starvation in computed tomography, which occurs when insufficient photon counts allow electronic noise to dominate the signal, leads to severe degradation in reconstructed images. This paper proposes a pre-correction method that combines a negative feedback mechanism with an adaptive diffusion filter to mitigate photon-starved effects by suppressing electronic noise in the sinogram prior to logarithmic transformation. The method was evaluated using ultra-low-dose scans of an anthropomorphic torso phantom and clinical patient data. For comparison, several sinogram-based denoising methods were also applied. The proposed method produced reconstructed images with the lowest noise, highest structural similarity, and superior spatial resolution, along with significantly reduced streaking and bias artifacts. Experimental results demonstrate that the proposed method effectively suppresses noise, streaking artifacts and large-scale bias artifacts in low-signal anatomical regions under severe photon starvation in low-dose conditions, while maintaining acceptable resolution.