  The Task Group 211 report of the American Association of Physicists in
Medicine (AAPM) reviewed static segmentation techniques in nuclear
positronemission tomography (PET) imaging used in nuclear medicine. These
methods, when applied to a dynamic image, such as the ones obtained in
pharmacokinetic analyses, fail to take into account the dynamic nature of the
acquisitions. In this article, the leading hypothesis was that a static
segmentation was not adequate in even the simplest dynamic PET images. To put
this idea forward, a simple dynamic PET phantom was devised. Many dynamic
acquisitions were obtained using FDG. To analyze them, different static
segmentations were performed on each timeframe. These were followed by
quantitative analyses to determine whether the segmentations were consistant
between various timeframes of reference. The quantitative analytical tools used
were the S{\o}rensen-Dice coefficients, the overlapping of the time-activity
curves (TACs), and the pharmacokinetic parameters extracted from the images
using the Dynesty Python package. In order to perform some of the analyses, an
uncertainty had to be added to the TACs themselves: to do so, the individual
segmentations were spatially displaced to estimate the sensibility of the TAC
to the underlying segmentation. Using these analytical tools, we propose that
static segmentations are not sufficient tools for segmenting dynamic images in
a nuclear medicine context. The specific case of pharmacokinetic modelling is
used to exemplify this idea. Further work could include a method of estimating
uncertainties on segmentations or a novel method for dynamic segmentations in
dynamic PET images.
