Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox
dc.bibliographicCitation.journalTitle | Technical report // Weierstraß-Institut für Angewandte Analysis und Stochastik | eng |
dc.bibliographicCitation.volume | 11 | |
dc.contributor.author | Hoffmann, Devy | |
dc.contributor.author | Tabelow, Karsten | |
dc.date.accessioned | 2016-03-24T17:38:47Z | |
dc.date.available | 2019-06-28T08:07:14Z | |
dc.date.issued | 2008 | |
dc.description.abstract | There exists a variety of software tools for analyzing functional Magnetic Resonance Imaging data. A very popular one is the freely available SPM package by the Functional Imaging Laboratory at the Wellcome Department of Imaging Neuroscience. In order to enhance the signal-to-noise ratio it provides the possibility to smooth the data in a pre-processing step by a Gaussian filter. However, this comes at the cost of reducing the effective resolution. In a series of recent papers it has been shown, that using a structural adaptive smoothing algorithm based on the Propagation-Separation method allows for enhanced signal detection while preserving the shape and spatial extent of the activation areas. Here, we describe our implementation of this algorithm as a toolbox for SPM. | eng |
dc.description.version | publishedVersion | eng |
dc.format | application/pdf | |
dc.identifier.issn | 1618-7776 | |
dc.identifier.uri | https://doi.org/10.34657/3289 | |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/2483 | |
dc.language.iso | eng | eng |
dc.publisher | Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik | eng |
dc.rights.license | This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties. | eng |
dc.rights.license | Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. | ger |
dc.subject.ddc | 510 | eng |
dc.subject.other | Adaptive weights | eng |
dc.subject.other | local structure | eng |
dc.subject.other | local polynomial regression | eng |
dc.subject.other | propagation | eng |
dc.subject.other | separation | eng |
dc.title | Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox | eng |
dc.type | Report | eng |
dc.type | Text | eng |
tib.accessRights | openAccess | eng |
wgl.contributor | WIAS | eng |
wgl.subject | Mathematik | eng |
wgl.type | Report / Forschungsbericht / Arbeitspapier | eng |
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