Memorias de investigación
Artículos en revistas:
Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach
Año:2016
Áreas de investigación
  • Bioinstrumentación,
  • Equipo médico,
  • Tecnología electrónica y de las comunicaciones
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Knowledge-Based Systems
ISSN
0950-7051
Factor de impacto JCR
3,325
Información de impacto
Volumen
106
DOI
10.1016/j.knosys.2016.05.053
Número de revista
Desde la página
264
Hasta la página
273
Mes
AGOSTO
Ranking
?Computer Science, Artificial Intelligence?: 17/130 (Q1)
Esta actividad pertenece a memorias de investigación
Participantes
  • 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
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Tecnología de imágenes biomédicas
  • Departamento: Ingeniería Electrónica