  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.
These assumptions are used within hierarchical priors of the blind source
separation and deconvolution algorithm. Comparison of the resulting algorithms
with these priors is performed on synthetic dataset as well as on real datasets
from dynamic renal scintigraphy. It is shown that flexible non-parametric
priors improve estimation of response functions in both cases. MATLAB
implementation of the resulting algorithms is freely available for download.

Machine Learning