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Maximum likelihood estimation for rician distributed data in analytical q-ball imaging

Beladi, Somaieh, Pathirana, Pubudu N. and Brotchie, Peter 2010, Maximum likelihood estimation for rician distributed data in analytical q-ball imaging, in EMBC 2010 : Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society “Merging Medical Humanism and Technology”, I E E E, Piscataway, N.J., pp. 2702-2705.

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Title Maximum likelihood estimation for rician distributed data in analytical q-ball imaging
Author(s) Beladi, Somaieh
Pathirana, Pubudu N.
Brotchie, Peter
Conference name Annual International Conference of the IEEE Engineering in Medicine and Biology Society (32nd : 2010 : Buenos Aires, Argentina)
Conference location Buenos Aires, Argentina
Conference dates 31 Aug. - 4 Sep. 2010
Title of proceedings EMBC 2010 : Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society “Merging Medical Humanism and Technology”
Editor(s) [Unknown]
Publication date 2010
Conference series Institute of Electrical and Electronics Engineers Engineering in Medicine and Biology Society International Conference
Start page 2702
End page 2705
Total pages 4
Publisher I E E E
Place of publication Piscataway, N.J.
Summary Analytical q-ball imaging is widely used for reconstruction of orientation distribution function (ODF) using diffusion weighted MRI data. Estimating the spherical harmonic coefficients is a critical step in this method. Least squares (LS) is widely used for this purpose assuming the noise to be additive Gaussian. However, Rician noise is considered as a more appropriate model to describe noise in MR signal. Therefore, the current estimation techniques are valid only for high SNRs with Gaussian distribution approximating the Rician distribution. The aim of this study is to present an estimation approach considering the actual distribution of the data to provide reliable results particularly for the case of low SNR values. Maximum likelihood (ML) is investigated as a more effective estimation method. However, no closed form estimator is presented as the estimator becomes nonlinear for the noise assumption of the Rician distribution. Consequently, the results of LS estimator is used as an initial guess and the more refined answer is achieved using iterative numerical methods. According to the results, the ODFs reconstructed from low SNR data are in close agreement with ODFs reconstructed from high SNRs when Rician distribution is considered. Also, the error between the estimated and actual fiber orientations was compared using ML and LS estimator. In low SNRs, ML estimator achieves less error compared to the LS estimator.
ISBN 9781424441242
ISSN 1557-170X
Language eng
Field of Research 110999 Neurosciences not elsewhere classified
Socio Economic Objective 920199 Clinical Health (Organs, Diseases and Abnormal Conditions) not elsewhere classified
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30030394

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
School of Engineering
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