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Enhancement of Fibre Orientation Distribution Reconstruction in Diffusion Weighted Imaging by Single Channel Blind Source Separation

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Jing, Min, McGinnity, TM, Coleman, SA, Zhang, Huaizhong, Fuchs, Armin and Kelso, JAS (2012) Enhancement of Fibre Orientation Distribution Reconstruction in Diffusion Weighted Imaging by Single Channel Blind Source Separation. IEEE Transactions on Biomedical Engineering, 59 (2). pp. 363-373. [Journal article]

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DOI: 10.1109/TBME.2011.2172793

Abstract

Abstract—In diffusion weighted imaging (DWI), reliable fibre tracking result relies on the accurate reconstruction of the fibre orientation distribution function (fODF) in each individualvoxel. For high angular resolution diffusion imaging (HARDI), deconvolution based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fibre populations.However HARDI based methods usually require relatively high b-values and a large number of gradient directions to reach good results. Such requirements are not always easy to meetin common clinical studies due to limitations in MRI facilities. Apart from this, most of these approaches are sensitivity to noise. In this study, we propose a new framework to enhancethe performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom data sets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results from in vivo data have shown that the proposed method has the potential to be applied to low angularresolution DWI which is commonly used in clinical studies.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:18709
Deposited By:Professor Martin McGinnity
Deposited On:11 May 2012 15:33
Last Modified:11 May 2012 15:33

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