Non-parametric Bayesian models of response function in dynamic image sequences

0456983 - ÚTIA 2017 RIV NL eng J - Journal Article
Tichý, Ondřej - Šmídl, Václav
Non-parametric Bayesian models of response function in dynamic image sequences.
Computer Vision and Image Understanding. Roč. 151, č. 1 (2016), s. 90-100. ISSN 1077-3142. E-ISSN 1090-235X
R&D Projects: GA ČR GA13-29225S
Institutional support: RVO:67985556
Keywords : Response function * Blind source separation * Dynamic medical imaging * Probabilistic models * Bayesian methods
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 2.498, year: 2016
http://library.utia.cas.cz/separaty/2016/AS/tichy-0456983.pdf

Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can not be observed directly and their estimation is complicated because the recorded images are subject to superposition of underlying signals. Therefore, the response functions are estimated via blind source separation and deconvolution. Performance of this algorithm heavily depends on the used models of the response functions. Response functions in real image sequences are rather complicated and finding a suitable parametric form is problematic. In this paper, we study estimation of the response functions using non-parametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness.
Permanent Link: http://hdl.handle.net/11104/0258398