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  Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

Becker, S. M., Tabelow, K., Voss, H. U., Anwander, A., Heidemann, R. M., & Polzehl, J. (2012). Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS). Medical Image Analysis, 16(6), 1142-1155. doi:10.1016/j.media.2012.05.007.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-9F9E-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-B120-0
Genre: Journal Article

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Becker_2012_Position-orientation.pdf (Publisher version), 4MB
 
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 Creators:
Becker, Saskia M.A.1, Author
Tabelow, Karsten1, Author
Voss, Henning U.2, Author
Anwander, Alfred3, Author              
Heidemann, Robin M.4, Author              
Polzehl, Jörg1, Author
Affiliations:
1Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ou_persistent22              
2Citigroup Biomedical Imaging Center, Weill Cornell Medical College, New York, NY, USA, ou_persistent22              
3Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
4Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              

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Free keywords: Diffusion weighted magnetic resonance imaging; POAS; Structural adaptive smoothing; Special Euclidean motion group; Lie groups
 Abstract: We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

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Language(s): eng - English
 Dates: 2012-05-112012-12-082012-05-112012-05-232012-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.media.2012.05.007
PMID: 22677817
Other: Epub 2012
 Degree: -

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Title: Medical Image Analysis
  Other : Med. Image Anal.
Source Genre: Journal
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Publ. Info: London : Elsevier
Pages: 14 Volume / Issue: 16 (6) Sequence Number: - Start / End Page: 1142 - 1155 Identifier: ISSN: 1361-8415
CoNE: /journals/resource/954927741859