PuSH - Publication Server of Helmholtz Zentrum München: Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Parker, G.J.M.* ; Schnabel, J.A.* ; Symms, M.R.* ; Werring, D.J.* ; Barker, G.J.*

Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.

J. Magn. Reson. Imaging 11, 702-710 (2000)

DOI

Open Access Green as soon as Postprint is submitted to ZB.

Abstract
Metrics
Extra information

Calculation and sorting of the eigenvectors of diffusion using diffusion tensor imaging has previously been shown to be sensitive to noise levels in the acquired data. This sensitivity manifests as random and systematic errors in the diffusion eigenvalues and derived parameters such as indices of anisotropy. An optimized application of nonlinear smoothing techniques to diffusion data prior to calculation of the diffusion tensor is shown to reduce both random and systematic errors, while causing little blurring of anatomical structures. Conversely, filtering applied to calculated images of fractional anisotropy is shown to fail in reducing systematic errors and in recovering anatomical detail. Using both real and simulated brain data sets, it is demonstrated that this approach has the potential to allow acquisition of data that would otherwise be too noisy to be of use. (C) 2000 Wiley-Liss, Inc.

Impact Factor

Scopus SNIP

Scopus
Cited By

Altmetric

0.000

1.489

119