Noise reduction in low-dose positron emission tomography with adaptive parameter estimation in sinogram domain

Abstract

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.