Incorporating Independent Component Analysis to Q-Ball Imaging for Diffusion Orientation Distribution Reconstruction

Min Jing, TM McGinnity, SA Coleman, Huaizhong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we investigate the incorporation of in-dependent component analysis (ICA) with Q-ball imaging (QBI)to extract information on the diffusion orientation distributionfunction (ODF) from an inner voxel. In our approach, the ICAalgorithm is applied to a mixture of ODFs which are constructedbased on the analytical QBI solution. The numerical simulationresults demonstrate that the proposed ICA framework can notonly successfully separate the diffusion ODF from the noisydiffusion data, but also achieves better performance comparedwith a QBI solution when the data has a low signal to noise ratio (SNR).
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages2706-2709
Number of pages4
DOIs
Publication statusPublished - 31 Aug 2010
Event32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Buenos Aires, Argentina
Duration: 31 Aug 2010 → …

Conference

Conference32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period31/08/10 → …

Fingerprint

Independent component analysis
Imaging techniques
Signal to noise ratio

Cite this

@inproceedings{2549815f8b0347ad8c2db2d9beccae94,
title = "Incorporating Independent Component Analysis to Q-Ball Imaging for Diffusion Orientation Distribution Reconstruction",
abstract = "In this paper, we investigate the incorporation of in-dependent component analysis (ICA) with Q-ball imaging (QBI)to extract information on the diffusion orientation distributionfunction (ODF) from an inner voxel. In our approach, the ICAalgorithm is applied to a mixture of ODFs which are constructedbased on the analytical QBI solution. The numerical simulationresults demonstrate that the proposed ICA framework can notonly successfully separate the diffusion ODF from the noisydiffusion data, but also achieves better performance comparedwith a QBI solution when the data has a low signal to noise ratio (SNR).",
author = "Min Jing and TM McGinnity and SA Coleman and Huaizhong Zhang",
year = "2010",
month = "8",
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doi = "10.1109/IEMBS.2010.5626529",
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Jing, M, McGinnity, TM, Coleman, SA & Zhang, H 2010, Incorporating Independent Component Analysis to Q-Ball Imaging for Diffusion Orientation Distribution Reconstruction. in Unknown Host Publication. pp. 2706-2709, 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 31/08/10. https://doi.org/10.1109/IEMBS.2010.5626529

Incorporating Independent Component Analysis to Q-Ball Imaging for Diffusion Orientation Distribution Reconstruction. / Jing, Min; McGinnity, TM; Coleman, SA; Zhang, Huaizhong.

Unknown Host Publication. 2010. p. 2706-2709.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - McGinnity, TM

AU - Coleman, SA

AU - Zhang, Huaizhong

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N2 - In this paper, we investigate the incorporation of in-dependent component analysis (ICA) with Q-ball imaging (QBI)to extract information on the diffusion orientation distributionfunction (ODF) from an inner voxel. In our approach, the ICAalgorithm is applied to a mixture of ODFs which are constructedbased on the analytical QBI solution. The numerical simulationresults demonstrate that the proposed ICA framework can notonly successfully separate the diffusion ODF from the noisydiffusion data, but also achieves better performance comparedwith a QBI solution when the data has a low signal to noise ratio (SNR).

AB - In this paper, we investigate the incorporation of in-dependent component analysis (ICA) with Q-ball imaging (QBI)to extract information on the diffusion orientation distributionfunction (ODF) from an inner voxel. In our approach, the ICAalgorithm is applied to a mixture of ODFs which are constructedbased on the analytical QBI solution. The numerical simulationresults demonstrate that the proposed ICA framework can notonly successfully separate the diffusion ODF from the noisydiffusion data, but also achieves better performance comparedwith a QBI solution when the data has a low signal to noise ratio (SNR).

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DO - 10.1109/IEMBS.2010.5626529

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