Speaker
Description
Long axial field-of-view PET scanners provide a significant gain in sensitivity, but also bring a substantially higher background of scattered and accidental coincidences. We investigate machine-learning classification of individual coincidence events using GATE Monte Carlo simulations of the Siemens Quadra scanner with NEMA IEC and anthropomorphic XCAT phantoms, where every event has a ground-truth label.
We evaluate XGBoost, AdaBoost, and neural network classifiers with two feature sets, and show that phantom-wise metrics give a misleading picture of classifier performance. In a cross-phantom test, a small decrease in accuracy corresponds to regionally concentrated degradation that only per-voxel quality maps reveal, while geometry-dependent features overfit the phantom geometry.
In our initial approach, we kept events predicted as true and rejected all others. We present two extensions in which the per-event probabilities influence the reconstructed image and its uncertainty. First, using soft probability weighting, in which each event contributes to reconstruction according to its estimated true-coincidence probability, we reduced the image-wise reconstruction error compared to hard filtering. Second, we apply split conformal prediction, which replaces a single predicted class with a set of classes carrying a finite-sample coverage guarantee. Aggregated per voxel, these sets produce ambiguity maps that identify unreliable image regions without ground-truth labels.
