Nonlocal mean (NLM) filtering proved to be an effective tool for noise reduction in natural and medical imaging. The technique relies on existing redundant information in the input image to discriminate the genuine signal from noise. However, due to the prohibitively long computation time, the search for finding similar information is confined by a predefined search window, which may hamper the performance of this filter. In this work, a spatially guided non local mean (SG-NLM) approach was proposed to overcome this issue. The proposed method was evaluated on whole-body positron emission tomography images presenting with high noise levels, which adversely affect lesion detectability and quantitative accuracy.