Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio 43210, USA.
Magn Reson Med. 2010 Mar;63(3):782-9. doi: 10.1002/mrm.22258.
Accurate measurement of spatially variant noise in MR images acquired using parallel imaging techniques is challenging. Image-based noise measurement methods such as the subtraction method proposed by the National Electrical Manufacturers Association or the multiple acquisition method often cannot be applied in vivo due to motion and/or dynamic contrast changes. Based on the Karhunen-Loeve transform and random matrix theory, we propose a novel method to accurately assess the noise variance in image series bearing temporal redundancy. The method fits the probability density function of eigenvalues from the temporal covariance matrix of the image series to the Marcenko-Pastur distribution. The accuracy of our method was validated using numerical simulation and an MR noise measurement experiment. The ability of this method to derive the g-factor map of a static phantom was validated against the multiple acquisition method. The method was applied to in vivo cardiac and brain image series and the results agreed with subtraction and multiple acquisition methods, respectively. This new image-based noise measurement method provides a practical means of retrospectively evaluating the noise level and/or g-factor map from multiframe image series.
使用并行成像技术获取的 MR 图像中空间变化噪声的精确测量具有挑战性。基于图像的噪声测量方法，如由美国国家电器制造商协会提出的减法方法或多次采集方法，由于运动和/或动态对比变化，通常无法在体内应用。基于 Karhunen-Loeve 变换和随机矩阵理论，我们提出了一种新方法，可以准确评估具有时间冗余的图像序列中的噪声方差。该方法将图像序列时间协方差矩阵的特征值的概率密度函数拟合到 Marcenko-Pastur 分布。我们的方法的准确性通过数值模拟和 MR 噪声测量实验进行了验证。该方法用于静态体模的 g 因子图的推导，并与多次采集方法进行了比较。该方法应用于体内心脏和脑部图像序列，结果分别与减法和多次采集方法一致。这种新的基于图像的噪声测量方法为从多帧图像序列中回顾性评估噪声水平和/或 g 因子图提供了一种实用手段。
