Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach
Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach
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DOI：
10.1109/tpami.2016.2625789
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发表时间：
2017-10
影响因子：
23.6
通讯作者：
Tomasz Pieciak;S. Aja‐Fernández;Gonzalo Vegas-Sánchez-Ferrero
中科院分区：
文献类型：
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作者：
Tomasz Pieciak;S. Aja‐Fernández;Gonzalo Vegas-Sánchez-Ferrero
并行磁共振成像（pMRI）技术由于能够大大加快图像采集过程，近年来在科研和临床领域都得到了广泛的应用。然而，校正二次采样伪影所需的图像重建算法影响噪声的性质，即，它变得不稳定。在文献中已经提出了一些处理pMRI中的非平稳噪声的方法。然而，它们的性能取决于通常不可用的信息，例如多次采集、接收器噪声矩阵、灵敏度线圈轮廓、重建系数或甚至数据的生物物理模型。此外，一些方法显示出不期望的颗粒模式的估计作为局部估计的副作用。最后，一些方法做出仅在高信噪比（SNR）的情况下成立的强假设，这限制了它们在真实的场景中的可用性。我们提出了一种新的自动噪声估计技术的非平稳莱斯噪声，克服了上述缺点。它的有效性是由于方差稳定变换的推导，旨在处理任何SNR。在合成和真实的场景中，将该方法与主要的最先进的方法进行了比较。数值结果证实了该方法的鲁棒性和更好的性能为整个范围的信噪比。
Parallel magnetic resonance imaging (pMRI) techniques have gained a great importance both in research and clinical communities recently since they considerably accelerate the image acquisition process. However, the image reconstruction algorithms needed to correct the subsampling artifacts affect the nature of noise, i.e., it becomes non-stationary. Some methods have been proposed in the literature dealing with the non-stationary noise in pMRI. However, their performance depends on information not usually available such as multiple acquisitions, receiver noise matrices, sensitivity coil profiles, reconstruction coefficients, or even biophysical models of the data. Besides, some methods show an undesirable granular pattern on the estimates as a side effect of local estimation. Finally, some methods make strong assumptions that just hold in the case of high signal-to-noise ratio (SNR), which limits their usability in real scenarios. We propose a new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks. Its effectiveness is due to the derivation of a variance-stabilizing transformation designed to deal with any SNR. The method was compared to the main state-of-the-art methods in synthetic and real scenarios. Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.