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

Pieciak, Tomasz; Vegas Sánchez Ferrero, Gonzalo y 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.

Descripción

Título: Non-stationary rician noise estimation in parallel MRI using a single image: A variance-stabilizing approach
Autor/es:
  • Pieciak, Tomasz
  • Vegas Sánchez Ferrero, Gonzalo
  • Aja Fernández, Santiago
Tipo de Documento: Artículo
Título de Revista/Publicación: Ieee Transactions on Pattern Analysis And Machine Intelligence
Fecha: Octubre 2017
Volumen: 39
Materias:
Palabras Clave Informales: Magnetic resonance imaging, Rician channels, Estimation, Image reconstruction, Receivers, Sensitivity,Data models
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaTEC2013-44194-PSin especificarSin especificarSin especificar
FP7291820MVISIONFUNDACION PARA EL CONOCIMIENTO MADRIMASDMVISION

Más información

ID de Registro: 50724
Identificador DC: http://oa.upm.es/50724/
Identificador OAI: oai:oa.upm.es:50724
Identificador DOI: 10.1109/TPAMI.2016.2625789
URL Oficial: https://ieeexplore.ieee.org/document/7736984/
Depositado por: Memoria Investigacion
Depositado el: 21 May 2018 18:06
Ultima Modificación: 21 May 2018 18:06
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