Bayesian Blind Source Separation in Dynamic Medical Imaging

0452447 - ÚTIA 2016 CZ eng D - Thesis
Tichý, Ondřej
Bayesian Blind Source Separation in Dynamic Medical Imaging.
Institute of Information Theory and Automation. Defended: Praha. 8.12.2015. - Praha: CTU v Praze, 2015. 120 s.
R&D Projects: GA ČR GA13-29225S
Institutional support: RVO:67985556
Keywords : Blind Source Separation * Variational Bayes Approximation * Sparsity * Deconvolution * Image Sequence
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2016/AS/tichy-0452447.pdf

This work is concerned with the blind source separation (BSS) problem in dynamic medical imaging with focus on dynamic planar renal scintigraphy. A common problem of imaging of a three-dimensional object into an image plane is that the signal arises as a superposition of signals from underlaying sources from different depths of a body. The task is to separate individual sources representing functional tissues in medical imaging, i.e. their images and activities over the time. The main contribution of this work is introduction of novel models of hierarchical priors for Bayesian BSS, development of BSS algorithms for them, and evaluation of their suitability on clinical data. Common method in this application domain is still manual analysis by an expert. Existing knowledge of the expert in dynamic nuclear medicine was used as inspiration for the proposed hierarchical priors. Two key studied properties of the problem are sparsity of the sources and modeling of each source activity as a convolution of the common input function and a source-specific convolution kernel.
Permanent Link: http://hdl.handle.net/11104/0257078