Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Denoising spinal cord fMRI data: Approaches to acquisition and analysis

Eippert, F., Kong, Y., Jenkinson, M., Tracey, I., & Brooks, J. C. W. (2017). Denoising spinal cord fMRI data: Approaches to acquisition and analysis. NeuroImage, 154, 255-266. doi:10.1016/j.neuroimage.2016.09.065.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Eippert, Falk1, Autor           
Kong, Yazhuo1, Autor
Jenkinson, Mark1, Autor
Tracey, Irene1, Autor
Brooks, Jonathan C. W.2, Autor
Affiliations:
1Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom, ou_persistent22              
2Clinical Research and Imaging Centre, University of Bristol, United Kingdom, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Functional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies. In this review, we give an overview of the various methods that have been used to address the technical challenges in spinal fMRI, with a focus on reducing the impact of physiological noise. We start out by describing acquisition methods that have been tailored to the special needs of spinal fMRI and aim to increase the signal-to-noise ratio and reduce distortion in obtained images. Following this, we concentrate on image processing and analysis approaches that address the detrimental effects of noise. While these include variations of standard pre-processing methods such as motion correction and spatial filtering, the main focus lies on denoising techniques that can be applied to task-based as well as resting-state data sets. We review both model-based approaches that rely on externally acquired respiratory and cardiac signals as well as data-driven approaches that estimate and correct for noise using the data themselves. We conclude with an outlook on techniques that have been successfully applied for noise reduction in brain imaging and whose use might be beneficial for fMRI of the human spinal cord.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2016-09-272016-09-282017-07-01
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.neuroimage.2016.09.065
PMID: 27693613
Anderer: Epub 2016
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden: ausblenden:
Projektname : -
Grant ID : G0700399 ; G0700238
Förderprogramm : -
Förderorganisation : Medical Research Council (MRC)

Quelle 1

einblenden:
ausblenden:
Titel: NeuroImage
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Orlando, FL : Academic Press
Seiten: - Band / Heft: 154 Artikelnummer: - Start- / Endseite: 255 - 266 Identifikator: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166