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SUMMARY:Diffusion Priors for Physics-Informed CT and PET Reconstruction
DTSTART;VALUE=DATE-TIME:20260909T070000Z
DTEND;VALUE=DATE-TIME:20260909T074000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191402Z
UID:indico-contribution-2042@events.ncbj.gov.pl
DESCRIPTION:Speakers: Alexandre Bousse (LaTIM U1101)\nGenerative models pr
 ovide powerful learned priors for solving challenging inverse problems in 
 medical imaging. In this talk\, I will present diffusion-based\, physics-i
 nformed approaches for CT and PET reconstruction\, illustrated through thr
 ee applications: motion-compensated head cone-beam CT\, material decomposi
 tion in photon-counting CT\, and joint activity-attenuation reconstruction
  in CT-less PET. These examples demonstrate how learned image priors can b
 e combined with measurement physics to achieve high-quality\, data-consist
 ent reconstruction without paired training data.\n\nhttps://events.ncbj.go
 v.pl/event/468/contributions/2042/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2042/
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