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SUMMARY:Machine Learning for Event-Level Background Rejection in LAFOV PET
DTSTART;VALUE=DATE-TIME:20260909T094000Z
DTEND;VALUE=DATE-TIME:20260909T101000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191404Z
UID:indico-contribution-2051@events.ncbj.gov.pl
DESCRIPTION:Speakers: Michał Obara (NCBJ)\nLong axial field-of-view PET s
 canners provide a significant gain in sensitivity\, but also bring a subst
 antially higher background of scattered and accidental coincidences. We in
 vestigate machine-learning classification of individual coincidence events
  using GATE Monte Carlo simulations of the Siemens Quadra scanner with NEM
 A IEC and anthropomorphic XCAT phantoms\, where every event has a ground-t
 ruth label.\n\nWe evaluate XGBoost\, AdaBoost\, and neural network classif
 iers with two feature sets\, and show that phantom-wise metrics give a mis
 leading picture of classifier performance. In a cross-phantom test\, a sma
 ll decrease in accuracy corresponds to regionally concentrated degradation
  that only per-voxel quality maps reveal\, while geometry-dependent featur
 es overfit the phantom geometry.\n\nIn our initial approach\, we kept even
 ts predicted as true and rejected all others.  We present two extensions i
 n 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-coinci
 dence probability\, we reduced the image-wise reconstruction error compare
 d 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 ambig
 uity maps that identify unreliable image regions without ground-truth labe
 ls.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/2051/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2051/
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