A Method for Enhancing PET Scan Images Using Nonlocal Mean Filter

Abstract

Medical images are an important source of information for both diagnosing and treating diseases. In many cases, the images produced by a Positron Emission Tomography (PET) scan are used to assess the effectiveness of a particular treatment. This paper presents a method for whole-body PET image denoising using a spatially-guided non-local means filter. The proposed method starts with clustering the images into regions. To estimate the noise, a Bayesian with automatic settings of the parameters was used. Then, only patches that belong to regions were collected and processed. The performance was compared to two methods; Gaussian and conventional Non-Local Means (NLM). The Jaszczak phantom and PET/ Computed Tomography (CT) for whole-body were involved in the benchmarking. The obtained results showed that in the Jaszczak phantom, the Signal-to-Noise Ratio (SNR) was significantly improved. Additionally, the proposed method improved the contrast and SNR compared to conventional NLM and Gaussian. Finally, the proposed method, in clinical whole-body PET, can be considered as another way of the post-reconstruction filter.