A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
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 via arterial blood sampling. However, arterial cannulation in small
animals like 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, potentially eliminating the need for blood sampling
in dynamic small-animal PET. The proposed FC-DLIF model includes a spatial
feature extractor acting on the volumetric time frames of the PET 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 [$^{18}$F]FDG data using cross validation. Further, the model
applicability is evaluated on imaging data and arterial blood curves collected
using two additional radiotracers ([$^{18}$F]FDOPA, and [$^{68}$Ga]PSMA). The
model was further evaluated on data truncated and shifted in time, to simulate
shorter, and shifted, PET scans. 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. 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.
