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      Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach

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          Abstract

          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.

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          Author and article information

          Journal
          IEEE Transactions on Pattern Analysis and Machine Intelligence
          IEEE Trans. Pattern Anal. Mach. Intell.
          Institute of Electrical and Electronics Engineers (IEEE)
          0162-8828
          2160-9292
          October 2017
          October 1 2017
          : 39
          : 10
          : 2015-2029
          Article
          10.1109/TPAMI.2016.2625789
          27845653
          508875e0-9fa3-4820-8e9d-827873c24d35
          © 2017
          History

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