The impact of scan time on dynamic $$^{68}{\textrm{Ga}}$$-FAPI-04 total-body PET parametric imaging generated by deep learning models

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PURPOSE: To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric images from static PET. Compared with traditional methods, this approach requires only a single PET/computed tomography (CT) scan. However, current methods tend to employ static PET images captured at fixed scanning times without considering whether static PET images acquired at different scanning times impact the quality of the dynamic PET parametric images generated via deep learning. METHODS: A single-frame image from dynamic [Formula: see text]-FAPI-04 total-body PET can actually be regarded as a static PET image acquired during that specific period of the PET/CT scan. We extracted 5 frames of dynamic [Formula: see text]-FAPI-04 total-body PET at equal intervals, specifically frames 50, 76, 80, 86, and 92. Each frame of the dynamic [Formula: see text]-FAPI-04 total-body PET images was subsequently input into the deep learning model to obtain dynamic [Formula: see text]-FAPI-04 total-body PET parametric images. By comparing and analyzing the quality of dynamic PET parametric images, we attempted to determine the optimal scan time for static PET. RESULTS: The experimental results revealed that the dynamic [Formula: see text]-FAPI-04 total-body PET parametric image generated from the 58th frame of the dynamic PET image via the deep learning model exhibited the poorest performance. This may be because the diffusion of the radioactive tracer was not stable at the time of PET/CT scanning in frame 58, but it was relatively stable from frames 70-92. CONCLUSION: Static PET images acquired at different scanning times do indeed affect the quality of the dynamic [Formula: see text]-FAPI-04 total-body PET parametric images generated via deep learning. Specifically, when the radioactive tracer is unstable during the early stage of scanning, the quality of the dynamic [Formula: see text]-FAPI-04 total-body PET parametric images generated from static PET images via deep learning appears to be inferior.

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