BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Tomographic reconstruction from proton CT list-mode data using aut
 omatic differentiation
DTSTART;VALUE=DATE-TIME:20260910T081000Z
DTEND;VALUE=DATE-TIME:20260910T084000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191354Z
UID:indico-contribution-2069@events.ncbj.gov.pl
DESCRIPTION:Speakers: Aurélien Coussat ()\nProton CT is a promising alter
 native to X-ray CT for proton therapy treatment planning\, allowing direct
  estimation of the relative stopping power map within the patient body wit
 hout relying on conversion from Hounsfield units. Conventional list-mode p
 roton CT scanners measure the energy loss of each individual proton to est
 imate the integral of the relative stopping power along its path\, the wat
 er-equivalent path length\, but do not meet the requirements for clinical 
 use due to their low acquisition rates. An alternative proton CT scanner d
 esign was recently proposed where the time-of-flight of each proton is mea
 sured between two detectors located before and after the patient along the
  proton beam. The main advantages of this sandwich time-of-flight design a
 re its compactness and high acquisition rates. However\, conversion from s
 andwich time-of-flight data to water-equivalent path length is not possibl
 e. We present an iterative algorithm that leverages PyTorch's automatic di
 fferentiation engine to directly optimize the voxels in the image space. T
 he method is assessed and compared using Monte Carlo simulations.\n\nhttps
 ://events.ncbj.gov.pl/event/468/contributions/2069/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2069/
END:VEVENT
END:VCALENDAR