Speaker
Description
In PET medical imaging, the reconstruction of the spatial distribution
of the radiotracer in patient’s body is based on the photon pairs
grouped into time coincidences. Due to the limited resolution the selected
coincidences contain a fraction of events with a photon scattered in the
patient or detector material and photons accidentally registered in a coincidence.
Scatters and accidentals deteriorate the final image quality.
For a total-body scanner, the background level becomes a challenge.
First, the accidentals statistics increase roughly quadratic with the
scanner axial length. Second, the multiply scattered photons fraction is
more pronounced. Morover in J-PET scanner the signal registration is based on
the Compton scattering process, which makes the inter-detector scatters
harder to discriminate.
We apply supervised learning models to estimate the background
contribution. In particular, boosted decision trees and deep learning
neural networks are considered. The training and test samples are based
on GATE Monte Carlo simulations. Selection of optimal feature set and feature
transformations is performed. Performances of XGBoost, AdaBoost and
selected NN classifiers are compared with cut-based selection criteria.
Considered models are compared based on efficiency metrics. Finally,
preliminary comparison of reconstructed image quality is provided.
