Spatially variant noise estimation in MRI: A homomorphic approach
Spatially variant noise estimation in MRI: A homomorphic approach
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DOI：
10.1016/j.media.2014.11.005
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发表时间：
2015-02-01
影响因子：
10.9
通讯作者：
Vegas-Sanchez-Ferrero, Gonzalo
中科院分区：
文献类型：
--
作者：
Aja-Fernandez, Santiago;Pieciak, Tomasz;Vegas-Sanchez-Ferrero, Gonzalo
由于噪声特征对后处理算法的影响，MRI中噪声特征的可靠估计是一项非常重要的任务。在文献中已经提出了许多方法来从幅度信号中恢复噪声特征。然而，它们中的大多数假设平稳噪声模型，即，噪声的特征不随图像内部的位置而变化。当考虑现代扫描技术时，这种假设不成立，例如，在并行重建和强度校正的情况下。因此，必须找到新的噪声估计器来科普非平稳噪声。最近在文献中提出了一些方法。然而，它们需要多次采集或通常不可用的额外信息（生物物理模型、线圈灵敏度）。在这项工作中，我们克服了这个缺点，提出了一种新的方法，可以准确地估计噪声的非平稳参数，从一个单一的幅度图像。在推导中，我们认为噪声遵循非平稳莱斯分布，因为它是真实的采集中最常见的模型（例如，SENSE重建），尽管它可以很容易地推广到其他模型。所提出的方法利用同态分离的空间变化的噪声在两个方面：一个平稳的噪声项和一个低频信号，对应于x依赖的方差的噪声。然后通过具有Rician偏差校正的低通滤波来估计噪声的非平稳方差。在真实的和合成实验的结果证明了更好的性能和最低的误差方差的方法相比，国家的最先进的方法。(C)2014爱思唯尔有限公司版权所有。
The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.