Self-Supervised Learning for Physiologically-Based Pharmacokinetic
  Modeling in Dynamic PET
Francesca De Benetti Walter Simson Magdalini Paschali Hasan Sari Axel Romiger Kuangyu Shi Nassir Navab Thomas Wendler
Dynamic positron emission tomography imaging (dPET) provides temporally
resolved images of a tracer enabling a quantitative measure of physiological
processes. Voxel-wise physiologically-based pharmacokinetic (PBPK) modeling of
the time activity curves (TAC) can provide relevant diagnostic information for
clinical workflow. Conventional fitting strategies for TACs are slow and ignore
the spatial relation between neighboring voxels. We train a spatio-temporal
UNet to estimate the kinetic parameters given TAC from F-18-fluorodeoxyglucose
(FDG) dPET. This work introduces a self-supervised loss formulation to enforce
the similarity between the measured TAC and those generated with the learned
kinetic parameters. Our method provides quantitatively comparable results at
organ-level to the significantly slower conventional approaches, while
generating pixel-wise parametric images which are consistent with expected
physiology. To the best of our knowledge, this is the first self-supervised
network that allows voxel-wise computation of kinetic parameters consistent
with a non-linear kinetic model. The code will become publicly available upon
acceptance.