Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.

ABSTRACT:

Background

Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a

SUBMITTER: Maus J 

PROVIDER: S-EPMC11231129 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature