DocumentCode :
2926791
Title :
Incorporating independent component analysis to Q-ball imaging for diffusion orientation distribution reconstruction
Author :
Jing, M. ; McGinnity, T.M. ; Coleman, S. ; Zhang, H.
Author_Institution :
Intell. Syst. Res. Centre, Univ. of Ulster, Londonderry, UK
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
2706
Lastpage :
2709
Abstract :
In this paper, we investigate the incorporation of independent component analysis (ICA) with Q-ball imaging (QBI) to extract information on the diffusion orientation distribution function (ODF) from an inner voxel. In our approach, the ICA algorithm is applied to a mixture of ODFs which are constructed based on the analytical QBI solution. The numerical simulation results demonstrate that the proposed ICA framework can not only successfully separate the diffusion ODF from the noisy diffusion data, but also achieves better performance compared with a QBI solution when the data has a low signal to noise ratio (SNR).
Keywords :
biodiffusion; biomedical MRI; image reconstruction; independent component analysis; medical image processing; ICA; Q-ball imaging; QBI; diffusion orientation distribution reconstruction; independent component analysis; signal to noise ratio; Correlation; Diffusion tensor imaging; Harmonic analysis; Image reconstruction; Signal to noise ratio; Tensile stress; Algorithms; Brain; Computer Simulation; Diffusion; Humans; Models, Neurological; Models, Statistical; Models, Theoretical; Neurons; Principal Component Analysis; Signal Processing, Computer-Assisted;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5626529
Filename :
5626529
Link To Document :
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