title: Incorporating independent component analysis to Q-ball imaging for diffusion orientation distribution reconstruction.
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).
authors.affiliation: Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, UK. m.jing@ulster.ac.uk
authors.forename: M
authors.initials: M
authors.lastname: Jing
authors.forename: T M
authors.initials: TM
authors.lastname: McGinnity
authors.forename: S
authors.initials: S
authors.lastname: Coleman
authors.forename: H
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D004058:Diffusion; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009474:Neurons / Q000473:pathology*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted