Abstract
Language | English |
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Title of host publication | Unknown Host Publication |
Pages | 2706-2709 |
Number of pages | 4 |
DOIs | |
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 |
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Period | 31/08/10 → … |
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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 proceeding › Conference contribution
TY - GEN
T1 - Incorporating Independent Component Analysis to Q-Ball Imaging for Diffusion Orientation Distribution Reconstruction
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).
U2 - 10.1109/IEMBS.2010.5626529
DO - 10.1109/IEMBS.2010.5626529
M3 - Conference contribution
SN - 1557-170X
SP - 2706
EP - 2709
BT - Unknown Host Publication
ER -