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

Abstract

In a PET study, shortening of transmission scan time is highly desired for improving patient comfort and increasing scanner throughput. It necessitates a method that reduces statistical noise in attenuation correction factors (ACFs). The authors have evaluated non-linear Gaussian (NLG) filtering for smoothing transmission images reconstructed with filtered back-projection instead of using iterative reconstruction and segmentation methods. The NLG filtering operation is a variation of local weighted averaging in a neighborhood around a pixel, which weights are determined according to both distance in location and difference in pixel value. Several filtering steps with different NLG parameters can effectively reduce noise without losing structural information. The NLG smoothed transmission images are then forward projected to generate ACFs. Results with phantom and patient data suggested that the NLG filtering method is useful for attenuation correction using count-limited transmission data for both brain and whole-body PET studies.