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The Task Group 211 report of the American Association of Physicists inMedicine (AAPM) reviewed static segmentation techniques in nuclearpositronemission tomography (PET) imaging used in nuclear medicine. Thesemethods, when applied to a dynamic image, such as the ones obtained inpharmacokinetic analyses, fail to take into account the dynamic nature of theacquisitions. In this article, the leading hypothesis was that a staticsegmentation was not adequate in even the simplest dynamic PET images. To putthis idea forward, a simple dynamic PET phantom was devised. Many dynamicacquisitions were obtained using FDG. To analyze them, different staticsegmentations were performed on each timeframe. These were followed byquantitative analyses to determine whether the segmentations were consistantbetween various timeframes of reference. The quantitative analytical tools usedwere the S{\o}rensen-Dice coefficients, the overlapping of the time-activitycurves (TACs), and the pharmacokinetic parameters extracted from the imagesusing the Dynesty Python package. In order to perform some of the analyses, anuncertainty had to be added to the TACs themselves: to do so, the individualsegmentations were spatially displaced to estimate the sensibility of the TACto the underlying segmentation. Using these analytical tools, we propose thatstatic segmentations are not sufficient tools for segmenting dynamic images ina nuclear medicine context. The specific case of pharmacokinetic modelling isused to exemplify this idea. Further work could include a method of estimatinguncertainties on segmentations or a novel method for dynamic segmentations indynamic PET images.

Static Segmentations in Dynamic PET Images: The need for a new method