Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

González Jaime, Luis and Vegas-Sánchez Ferrero, Gonzalo and Kerre, Etienne E. and Aja Fernández, Santiago (2016). Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach. "Knowledge-Based Systems", v. 106 ; pp. 264-273. ISSN 0950-7051. https://doi.org/10.1016/j.knosys.2016.05.053.

Description

Title: Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach
Author/s:
  • González Jaime, Luis
  • Vegas-Sánchez Ferrero, Gonzalo
  • Kerre, Etienne E.
  • Aja Fernández, Santiago
Item Type: Article
Título de Revista/Publicación: Knowledge-Based Systems
Date: August 2016
ISSN: 0950-7051
Volume: 106
Subjects:
Freetext Keywords: MRI; Rician noise; SENSE; Non-stationary distributed noise; Consensus; Noise filtering
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a sub-sampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7238819MIBISOCUnspecifiedMedical Imaging Using Bio-inspired and Soft Computing
Government of SpainTEC2013-44194UnspecifiedMinisterio de Ciencia e InnovaciónUnspecified
FP7291820MVISIONUnspecifiedMVISION

More information

Item ID: 46269
DC Identifier: http://oa.upm.es/46269/
OAI Identifier: oai:oa.upm.es:46269
DOI: 10.1016/j.knosys.2016.05.053
Official URL: http://www.sciencedirect.com/science/article/pii/S0950705116301575
Deposited by: Memoria Investigacion
Deposited on: 03 Jun 2017 08:36
Last Modified: 22 Mar 2019 13:51
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