title: Functional MR image statistical restoration for neural activity detection using Hidden Markov Tree model.
abstract: In this paper, we present a framework for functional MR image restoration based on the Hidden Markov Tree (HMT) model. Under this scheme, the wavelet/contourlet coefficients of the distorted image are filtered using the HMT model of the baseline image to minimise the statistical divergence between two images. An iterative algorithm between image registration and HMT filtering is developed to achieve a trade-off between the least mean square error (in the spatial domain) and the minimum statistical divergence (in the spectral domain). We demonstrate that the proposed method can eliminate the motion artefacts (such as spikes and burring) in the Functional MR Imaging data more effectively, leading to reliable neural activity detection. This method can also be used for image restoration in other medical imaging applications.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
authors.forename: Chuan
authors.initials: C
authors.lastname: Li
authors.forename: Qi
authors.initials: Q
authors.lastname: Hao
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical