Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events

Arman Rahmim, Katie Dinelle, Ju Chieh Cheng, Mikhail A. Shilov, William P. Segars, Sarah C. Lidstone, Stephan Blinder, Olivier G. Rousset, Hamid Vajihollahi, Benjamin M.W. Tsui, Dean F. Wong, Vesna Sossi

Research output: Contribution to journalArticlepeer-review

Abstract

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the fleld-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

Original languageEnglish (US)
Article number4446612
Pages (from-to)1018-1033
Number of pages16
JournalIEEE transactions on medical imaging
Volume27
Issue number8
DOIs
StatePublished - Aug 2008

Keywords

  • Motion correction
  • Positron emission tomography (PET) iterative reconstruction
  • System response

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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