Denoising of Dynamic Sinogram by Image Guided Filtering for Positron Emission Tomography

Abstract

In order to improve the denoising performance in positron emission tomography (PET) images, various smoothing filters have been applied. Recent reports on PET denoising indicate that the signal-to-noise and contrast-to-noise ratios have been improved by sinogram-based predenoising. In this paper, we propose a sinogram-based dynamic image guided filtering (SDIGF) algorithm for noise reduction. The proposed algorithm uses a normalized static PET sinogram as the guidance image, acquiring the entire data from the start to end of the data acquisition. In the evaluation, dynamic PET simulation data and real dynamic data obtained from a living monkey brain using a [18F]fluoro-2-deoxy-D-glucose are used for comparing the SDIGF, image-based dynamic image guided filter, Gaussian filter (GF), and bilateral filter (BF). In the simulation data, the proposed algorithm improves the peak signal-to-noise ratio, as well as the structural similarity index, in all time frames, compared to the GF and BF algorithms. In the real data, the proposed algorithm subjectively reduces the statistical noise compared to the other algorithms. The SDIGF algorithm reduces statistical noise, while preserving the image edges, and improves the quantitative time activity curve accuracy, compared to the other algorithms. Thus, this paper demonstrates that the proposed algorithm is a simple and powerful denoising algorithm.