Non-stationary rician noise estimation in parallel MRI using a single image: A variance-stabilizing approach

Pieciak, Tomasz and Vegas Sánchez Ferrero, Gonzalo and Aja Fernández, Santiago (2017). Non-stationary rician noise estimation in parallel MRI using a single image: A variance-stabilizing approach. "Ieee Transactions on Pattern Analysis And Machine Intelligence", v. 39 (n. 10); pp. 2015-2029. ISSN 0162-8828. https://doi.org/10.1109/TPAMI.2016.2625789.

Description

Title: Non-stationary rician noise estimation in parallel MRI using a single image: A variance-stabilizing approach
Author/s:
  • Pieciak, Tomasz
  • Vegas Sánchez Ferrero, Gonzalo
  • Aja Fernández, Santiago
Item Type: Article
Título de Revista/Publicación: Ieee Transactions on Pattern Analysis And Machine Intelligence
Date: October 2017
ISSN: 0162-8828
Volume: 39
Subjects:
Freetext Keywords: Magnetic resonance imaging, Rician channels, Estimation, Image reconstruction, Receivers, Sensitivity,Data models
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-44194-PUnspecifiedUnspecifiedUnspecified
FP7291820MVISIONFUNDACION PARA EL CONOCIMIENTO MADRIMASDMVISION

More information

Item ID: 50724
DC Identifier: http://oa.upm.es/50724/
OAI Identifier: oai:oa.upm.es:50724
DOI: 10.1109/TPAMI.2016.2625789
Official URL: https://ieeexplore.ieee.org/document/7736984/
Deposited by: Memoria Investigacion
Deposited on: 21 May 2018 18:06
Last Modified: 21 May 2018 18:06
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