Optimally Stabilized PET Image Denoising Using Trilateral Filtering

TL;DR

This paper presents a novel PET image denoising method that stabilizes signal-dependent noise using generalized Anscombe's transformation and extends bilateral filtering to trilateral filtering, improving structure preservation and quantitative accuracy.

Contribution

The authors introduce a new PET denoising approach combining noise stabilization with trilateral filtering, addressing limitations of existing methods in preserving small structures and quantitative metrics.

Findings

Outperforms existing denoising techniques on diverse PET-CT images.

Effectively preserves structural boundaries while reducing noise.

Maintains quantitative metrics like SUV and lesion volume.

Abstract

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