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Publication details

Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors

Journal Article

Tichý Ondřej, Šmídl Václav


serial: IEEE Transactions on Medical Imaging vol.34, 1 (2015), p. 258-266

project(s): GA13-29225S, GA ČR

keywords: Functional imaging, Blind source separation, Computer-aided detection and diagnosis, Probabilistic and statistical methods

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abstract (eng):

A common problem of imaging three-dimensional objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved by the Variational Bayes method.

RIV: BB

2012-12-21 16:10