Locally adaptive filtering for edge preserving noise reduction on images with low SNR in PET

As well known, the signal-to-noise ratio (SNR) of PET images can be considerably low. This is especially true for whole-body examinations of heavy patients, for respiratory-gated studies, and dynamic studies with short frames. In these cases moving average filters (MAF) such as a Gaussian filter are applied in order to achieve an acceptable SNR. Image resolution is, however, considerably reduced by these MAFs. This affects detectability and quantification of small structures. Interesting alternatives to MAFs are non-linear, locally adaptive filters (NLF), which enable noise reduction while preserving sharp edges.