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. Attached Files Name Description MIMEType Size Downloads Title Maximum likelihood estimation for rician distributed data in analytical q-ball imaging Author(s) Beladi, Somaieh Pathirana, Pubudu N. orcid.org/0000-0001-8014-7798 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   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 28 Sep 2010, 17:52:17 EST Tue, 28 Sep 2010, 17:53:34 EST Tue, 28 Sep 2010, 17:58:24 EST Wed, 29 Sep 2010, 15:24:32 EST Wed, 29 Sep 2010, 15:25:19 EST Wed, 29 Sep 2010, 15:26:19 EST Wed, 29 Sep 2010, 15:26:54 EST Wed, 29 Sep 2010, 15:28:03 EST Wed, 29 Sep 2010, 15:29:10 EST Fri, 16 Mar 2012, 18:17:36 EST Wed, 21 Oct 2015, 13:49:17 EST Wed, 21 Oct 2015, 17:07:27 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 509 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Tue, 28 Sep 2010, 17:52:11 EST by Sandra Dunoon

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