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Functional Positron Emission Tomography (fPET) data offers novel insights into brain energy demands and molecular connectivity. Recent advances in improving temporal resolutions for this imaging technique have opened up new research possibilities. However, lower signal-to-noise ratios (SNR) inherent to short PET frames bring into question whether current realignment approaches still provide appropriate motion correction or whether additional processing steps might be required to improve correction outcomes. Thus, we aimed to evaluate the effectiveness of a standard motion correction procedure in conjunction with different SNR enhancement methods and explore potential improvements for high temporal resolution fPET with 3s dynamic frames. We investigated three techniques aimed at improving the SNR to facilitate more accurate realignment of fPET images, in comparison to conventional motion correction without any prior modifications. The methods include a deep learning technique based on the application of a conditional generative adversarial network, an exponentially weighted sliding window average and an established filtering method for denoising dynamic imaging data. Performance was compared and evaluated by correlating rigid motion parameters between approaches and with simultaneously acquired fMRI data, and by assessing magnitudes of task-induced activation. Our results indicate that none of the methods substantially improve mitigation of motion artefacts. Given the increased computational effort of the techniques, we propose that the standard motion correction procedure without prior signal modification is adequate for processing high temporal resolution fPET data of a healthy study population, exhibiting commonly observed amounts of movement. Nevertheless, future development of targeted strategies to enhance motion correction may further advance this imaging technique and applications thereof.