Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach
Year:2016
Research Areas
  • Bioinstrumentation,
  • Medical equipment,
  • Electronic technology and of the communications
Information
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 xdependent. 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.
International
Si
JCR
Si
Title
Knowledge-Based Systems
ISBN
0950-7051
Impact factor JCR
3,325
Impact info
Volume
106
10.1016/j.knosys.2016.05.053
Journal number
From page
264
To page
273
Month
AGOSTO
Ranking
?Computer Science, Artificial Intelligence?: 17/130 (Q1)
Participants
  • Autor: L Gonzalez-Jaime (Ghent University)
  • Autor: Gonzalo Vegas Sánchez-Ferrero (UPM)
  • Autor: E.E. Kerre (Ghent University)
  • Autor: S Aja-Fernandez (Universidad de Valladolid)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Tecnología de imágenes biomédicas
  • Departamento: Ingeniería Electrónica
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)