Rotation Invariant Non-Local Means for Noise Reduction in Magnetic Resonance Images

Abstract

Averaging of multiple scans is often used in magnetic resonance imaging (MRI) to increase the signal-to-noise ratio (SNR). However, image averaging often results in movement-induced blurs of the edges and tissue details. A matched and weighted averaging (MWA) method has been proposed by our group to obtain images with reduced blurring effects in signal averaging. Here a rotation-invariant non-local means (RINLM) algorithm was proposed, which used circular patches consisted of series of rings with equal area, instead of square patches, to search for similar patches in the images. Compared with the non-local means (NLM) algorithm, the RINLM algorithm was capable of finding more similar patches in the images containing many rotated local structure. This method was used to process noisy images to improve the SNR, and validated using both phantom images and in vivo MR images. The results demonstrated that the method could improve the SNR, while better preserving the edges and details of the images.

Keywords

• magnetic resonance imaging (MRI)
• non-local means (NLM)
• rotation invariance
• image denoising