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In dynamic reconstruction of positron emission tomography (PET) data a sequence of measured data sets is usually reconstructed independently from each other. Using this timeframe reconstruction, an appropriate trade-off between time resolution and noise has to be found. To overcome these drawbacks smoothing techniques and advanced dynamic reconstruction algorithms are more and more applied. Especially for the last, list-mode reconstruction is the predestinated approach, as the data are acquired in the highest possible spatial and temporal resolution. In this contribution we study dynamic reconstruction algorithms that base on the ML-EM algorithm for the small animal PET scanner ClearPETtradeNeuro. In a simulated example we generate list-mode data and compute time activity curves from the reconstructed images. We compare dynamic reconstruction methods, like time-frame reconstruction - with and without temporal smoothing - and reconstruction with B-splines as temporal basis functions.