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Noise Estimation in Multiple–Coil MR Data

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Statistical Analysis of Noise in MRI

Abstract

The estimators presented in the previous chapter were focused on single-coil acquisitions. Here, we extend those results to the particular case of multiple-coil acquisitions in which the magnitude signal is reconstructed using Sum of Squares (SoS) or a Spatial matched filter (SMF). In addition, we assume that there exist no correlations among coils, and all of them show the same variance of noise. As a consequence, the magnitude signal follows a stationary noncentral- \(\chi \) distribution (nc-\(\chi \)) if SoS is used, or a stationary Rician one, in the case of SMF. We focus on the SoS case and the nc-\(\chi \) distribution, since the Rician case is studied in Chap. 7. The main noise estimators for the nc-\(\chi \) are thus reviewed and classified. Most of the methods proposed are basically extrapolations of the Rician estimators to the nc-\(\chi \). In the last part of the chapter, different estimators are compared and a performance analysis is done using synthetic and real data.

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Notes

  1. 1.

    For multiple-coil the SNR is usually defined as \({\text {SNR}}=\frac{A_T(\mathbf{{x}})}{\sigma _L}=\frac{A_T(\mathbf{{x}})}{\sqrt{L}\ \sigma }\). Note that the SNR formulation in Eq. (8.15) shows an scaled definition: \({\text {SNR}}=\frac{A_T(\mathbf{{x}})}{\sigma }\).

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Correspondence to Santiago Aja-Fernández .

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© 2016 Springer International Publishing Switzerland

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Aja-Fernández, S., Vegas-Sánchez-Ferrero, G. (2016). Noise Estimation in Multiple–Coil MR Data. In: Statistical Analysis of Noise in MRI. Springer, Cham. https://doi.org/10.1007/978-3-319-39934-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-39934-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39933-1

  • Online ISBN: 978-3-319-39934-8

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