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Low-resolution and signal-dependent noise distribution in positron emissiontomography (PET) images makes denoising process an inevitable step prior toqualitative and quantitative image analysis tasks. Conventional PET denoisingmethods either over-smooth small-sized structures due to resolution limitationor make incorrect assumptions about the noise characteristics. Therefore,clinically important quantitative information may be corrupted. To addressthese challenges, we introduced a novel approach to remove signal-dependentnoise in the PET images where the noise distribution was considered asPoisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation(GAT) was used to stabilize varying nature of the PET noise. Other than noisestabilization, it is also desirable for the noise removal filter to preservethe boundaries of the structures while smoothing the noisy regions. Indeed, itis important to avoid significant loss of quantitative information such asstandard uptake value (SUV)-based metrics as well as metabolic lesion volume.To satisfy all these properties, we extended bilateral filtering method intotrilateral filtering through multiscaling and optimal Gaussianization process.The proposed method was tested on more than 50 PET-CT images from variouspatients having different cancers and achieved the superior performancecompared to the widely used denoising techniques in the literature.

Optimally Stabilized PET Image Denoising Using Trilateral Filtering