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SUMMARY:AI and Monte Carlo Particle Transport in Medical Imaging and Radio
 therapy
DTSTART;VALUE=DATE-TIME:20260911T083000Z
DTEND;VALUE=DATE-TIME:20260911T091000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191403Z
UID:indico-contribution-2060@events.ncbj.gov.pl
DESCRIPTION:Speakers: Nils Krah (INSA Lyon)\nMonte Carlo particle transpor
 t is a cornerstone of simulation in medical imaging and radiotherapy\, but
  its computational cost can limit its use. Artificial intelligence offers 
 several routes to accelerate these simulations\, from denoising low-statis
 tics results and predicting dose distributions to learning detector respon
 ses and generating particle phase spaces.\n\nThis talk surveys these appro
 aches through medical applications\, including nuclear imaging\, radiother
 apy dose calculation\, and optical photon transport in radiation detectors
 . It examines what each model learns\, which parts of the simulation it re
 places\, and how its output can be validated. Particular attention is give
 n to the distinction between reproducing an average response and preservin
 g the probability distributions and correlations needed for reliable simul
 ation.\n\nThe discussion also extends to AI-assisted radiotherapy planning
 \, where a predicted dose distribution must be converted into a physically
  deliverable treatment plan. Together\, these examples highlight the oppor
 tunities for combining AI with Monte Carlo methods and the importance of e
 valuating the complete workflow\, beyond prediction accuracy or computatio
 nal speed alone.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/206
 0/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2060/
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