Median non-local means filtering for low SNR image denoising: Application to PET with anatomical knowledge

Denoising low signal-to-noise-ratio (SNR) images is a significant challenge since the intensity gradient due to noise elements may compete with or even exceed the intensity gradient due to features in the images. This situation can often be encountered in photon-limited medical imaging applications such as MLEM reconstructed Positron Emission Tomography (PET) images. In this study, we propose a median non-local means filter for denoising low-SNR images. The proposed method incorporates a median filtering operation indirectly in the non-local means (NLM) method, which gives more robust estimation of the weights used to average the pixels in the image. For the application of multi-modality imaging such as PET/CT, we further extended the method to incorporate anatomical side information which can be obtained from co-registered CT images without segmentation to preserve abrupt changes between organs on PET images and reduce the computational cost of weight calculations. We applied the proposed method (AMNLM) to a PET/CT simulation, a real physical phantom study and a clinical patient study with lung lesions. The results suggest that the proposed method outperforms the standard Gaussian filtering approach, anisotropic-median diffusion filtering (AMDF) and NLM in terms of visual assessment and trade-off between lesion contrast and noise.