Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

Mikael Agn, Claus Svarer, Vibe G. Frokjaer, Douglas N. Greve, Gitte M. Knudsen, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.
Original languageEnglish
Title of host publicationProceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI 2014 )
PublisherIEEE
Publication date2014
Pages955-658
ISBN (Print)978-1-4673-1961-4
DOIs
Publication statusPublished - 2014
Event11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Beijing, China
Duration: 29 Apr 20142 May 2014
Conference number: 11
http://biomedicalimaging.org/2014/

Conference

Conference11th IEEE International Symposium on Biomedical Imaging
Number11
CountryChina
CityBeijing
Period29/04/201402/05/2014
Internet address

Keywords

  • PET
  • Bayesian modeling
  • multilinear reference tissue method
  • parametric imaging
  • regularization

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