Improving Undersampled MRI Reconstruction Using Non-local Means

Obtaining high quality images in MR is desirable not only for accurate visual assessment but also for automatic processing to extract clinically relevant parameters. Filtering-based techniques are extremely useful for reducing artifacts caused due to under sampling of k-space (to reduce scan time). The recently proposed Non-Local Means (NLM) filtering method offers a promising means to denoise images. Compared to most previous approaches, NLM is based on a more realistic model of images, which results in little loss of information while removing the noise. Here we extend the NLM method for MR image reconstruction from under sampled k-space data. The method is applied on T1-weighted images of the breast and T2-weighted anatomical brain images. Results show that NLM offers a promising method that can be used for accelerating MR data acquisitions.