旋转不变的非局域均值算法在磁共振图像去噪中的应用

磁共振成像（MRI）实验时常采用多次扫描累加平均提高图像信噪比（SNR），但当扫描过程中运动引起图像变形时，简单地累加平均就无法奏效.为此，本研究组曾提出一种匹配加权平均方法（MWA）提高图像的信噪比.在此基础上，该文提出一种旋转不变的非局域均值算法（RINLM），即选取圆形邻域区域并将其划分为一系列以中心像素为圆心的等面积圆环，再计算模式的相似性.RINLM算法可以更好地利用图像中旋转的冗余信息、找到更多的相似结构，提高算法的去噪性能.我们把该方法应用于低信噪比图像序列的平均和去噪中，可以更好地处理旋转的局部运动.与非局域均值算法（NLM）相比，RINLM算法可以进一步提高图像的信噪比；与MWA方法相比，其与RINLM算法的结合可以进一步提高磁共振图像序列信噪比，更好的保持图像边缘信息.

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

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.