  Dose verification based on proton-induced positron emitters is a promising
quality assurance tool and may leverage the strength of artificial
intelligence. To move a step closer towards practical application, the
sensitivity analysis of two factors needs to be performed: biological washout
and depth selection. selection. A bi-directional recurrent neural network (RNN)
model was developed. The training dataset was generated based upon a CT
image-based phantom (abdomen region) and multiple beam energies/pathways, using
Monte-Carlo simulation (1 mm spatial resolution, no biological washout). For
the modeling of biological washout, a simplified analytical model was applied
to change raw activity profiles over a period of 5 minutes, incorporating both
physical decay and biological washout. For the study of depth selection (a
challenge linked to multi field/angle irradiation), truncations were applied at
different window lengths (100, 125, 150 mm) to raw activity profiles. Finally,
the performance of a worst-case scenario was examined by combining both factors
(depth selection: 125 mm, biological washout: 5 mins). The accuracy was
quantitatively evaluated in terms of range uncertainty, mean absolute error
(MAE) and mean relative errors (MRE). Our proposed AI framework shows good
immunity to the perturbation associated with two factors. The detection of
proton-induced positron emitters, combined with machine learning, has great
potential to implement online patient-specific verification in proton therapy.
