PURPOSE We recently introduced a multispectral (MS) nonlocal (NL) filter based on maximum likelihood estimation (MLE) of voxel intensities, termed MS-NLML. While MS-NLML provides excellent noise reduction and improved image feature preservation as compared to other NL or MS filters, it requires considerable processing time, limiting its application in routine analyses. In this work, we introduced a fast, simple, and robust filter, termed nonlocal estimation of multispectral magnitudes (NESMA), for noise reduction in multispectral (MS) magnetic resonance imaging (MRI). METHODS Through extensive simulation and in-vivo analyses, we compared the performance of NESMA and MS-NLML in terms of noise reduction and processing efficiency. Further, we introduce two simple adaptive methods that permit spatial variation of similar voxels, R, used in the filtering. The first method is semi-adaptive and permits variation of R across the image by using a relative Euclidean distance (RED) similarity threshold. The second method is fully adaptive and filters the raw data with several RED similarity thresholds to spatially determine the optimal threshold value using an unbiased criterion. RESULTS NESMA shows very similar filtering performance as compared to MS-NLML, however, with much simple implementation and very fast processing time. Further, for both filters, the adaptive methods were shown to further reduce noise in comparison with the conventional non-adaptive method in which R is set to a constant value throughout the image. CONCLUSIONS NESMA is fast, robust, and straightforward to implement filter. These features render it suitable for routine clinical use and analysis of large MRI datasets.

中文翻译：

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一种简单快速的自适应非局部多谱滤波算法，可有效降低磁共振成像中的噪声。

目的最近我们基于体素强度的最大似然估计（MLE）引入了多光谱（MS）非局部（NL）滤波器，称为MS-NLML。尽管与其他NL或MS滤镜相比，MS-NLML可以提供出色的降噪效果并改善了图像特征保留能力，但它需要大量的处理时间，从而限制了其在常规分析中的应用。在这项工作中，我们引入了一种快速，简单且鲁棒的滤波器，称为多光谱幅度的非局部估计（NESMA），用于减少多光谱（MS）磁共振成像（MRI）中的噪声。方法通过广泛的仿真和体内分析，我们在降噪和处理效率方面比较了NESMA和MS-NLML的性能。此外，我们介绍了两种简单的自适应方法，这些方法允许相似体素R，用于过滤。第一种方法是半自适应的，并通过使用相对欧几里得距离（RED）相似度阈值允许R在整个图像上变化。第二种方法是完全自适应的，并使用几个RED相似性阈值过滤原始数据，以使用无偏准则在空间上确定最佳阈值。结果NESMA与MS-NLML相比显示出非常相似的过滤性能，但是实现起来非常简单且处理时间非常快。另外，对于两个滤波器，与其中在整个图像中将R设置为恒定值的常规非自适应方法相比，自适应方法被示出进一步降低了噪声。结论NESMA快速，强大且易于实现滤波器。