  Factor analysis has proven to be a relevant tool for extracting tissue
time-activity curves (TACs) in dynamic PET images, since it allows for an
unsupervised analysis of the data. Reliable and interpretable results are
possible only if considered with respect to suitable noise statistics. However,
the noise in reconstructed dynamic PET images is very difficult to
characterize, despite the Poissonian nature of the count-rates. Rather than
explicitly modeling the noise distribution, this work proposes to study the
relevance of several divergence measures to be used within a factor analysis
framework. To this end, the $\beta$-divergence, widely used in other
applicative domains, is considered to design the data-fitting term involved in
three different factor models. The performances of the resulting algorithms are
evaluated for different values of $\beta$, in a range covering Gaussian,
Poissonian and Gamma-distributed noises. The results obtained on two different
types of synthetic images and one real image show the interest of applying
non-standard values of $\beta$ to improve factor analysis.
