Noise reduction in PET attenuation correction using non-linear Gaussian filters

In a PET study, shortening of transmission scan time is highly desired for improving patient comfort and increasing scanner throughput. It necessitate s a method that reduces statistical noise in attenuation correction factors (ACFs). We have evaluated non-linear Gaussian (NLG) filtering for smoothin g transmission images reconstructed with filtered back-projection instead o f using iterative reconstruction and segmentation methods. The NLG filterin g operation is a variation of local weighted averaging in a neighborhood ar ound a pixel, which weights are determined according to both distance in lo cation and difference in pixel value. Several filtering steps with differen t NLG parameters can effectively reduce noise without losing structural inf ormation. The NLG smoothed transmission images are then forward projected t o generate ACFs. Results with phantom and patient data suggested that the N LG filtering method is useful for attenuation correction using count-limite d transmission data for both brain and whole-body PET studies.