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

Min Jing; TM McGinnity; SA Coleman; Huaizhong Zhang

doi:10.1109/IEMBS.2010.5626529

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 proceeding › Conference 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).

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	2706-2709
Number of pages	4
ISBN (Print)	1557-170X
DOIs	https://doi.org/10.1109/IEMBS.2010.5626529
Publication status	Published - 31 Aug 2010
Event	32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Buenos Aires, Argentina Duration: 31 Aug 2010 → …

Conference

Conference	32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period	31/08/10 → …

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10.1109/IEMBS.2010.5626529

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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",

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AU - Jing, Min

AU - McGinnity, TM

AU - Coleman, SA

AU - Zhang, Huaizhong

PY - 2010/8/31

Y1 - 2010/8/31

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

M3 - Conference contribution

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BT - Unknown Host Publication

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T2 - 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 31 August 2010

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