Background Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer

development research in small animal studies. Accurate kinetic modeling requires precise input function estimation,

traditionally achieved through arterial blood sampling. However, arterial cannulation in small animals, such as mice,

involves intricate, time-consuming, and terminal procedures, precluding longitudinal studies. This work proposes a

non-invasive, fully convolutional deep learning-based approach (FC-DLIF) to predict input functions directly from PET

imaging data, which may eliminate the need for arterial blood sampling in the context of dynamic small-animal PET

imaging. The proposed FC-DLIF model consists of a spatial feature extractor that acts on the volumetric time frames

of the dynamic PET imaging sequence, extracting spatial features. These are subsequently further processed in a

temporal feature extractor that predicts the arterial input function. The proposed approach is trained and evaluated

using images and arterial blood curves from [18F]FDG data using cross validation. Further, the model applicability is

evaluated on imaging data and arterial blood curves collected using two additional radiotracers ([18F]FDOPA, and

[68Ga]PSMA). The model was further evaluated on data truncated and shifted in time, to simulate shorter, and shifted,

PET scans.

Results The proposed FC-DLIF model reliably predicts the arterial input function with respect to mean squared error

and correlation. Furthermore, the FC-DLIF model is able to predict the arterial input function even from truncated and

shifted samples. The model fails to predict the AIF from samples collected using different radiotracers, as these are not

represented in the training data.

Conclusion Our deep learning-based input function offers a non-invasive and reliable alternative to arterial blood

sampling, proving robust and flexible to temporal shifts and different scan durations.

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