Optimized Bayes variational regularization prior for 3D PET images

Eugenio Rapisarda, Luca Presotto, Elisabetta De Bernardi, Maria Carla Gilardi, Valentino Bettinardi

Research output: Contribution to journalArticle

Abstract

A new prior for variational Maximum a Posteriori regularization is proposed to be used in a 3D One-Step-Late (OSL) reconstruction algorithm accounting also for the Point Spread Function (PSF) of the PET system. The new regularization prior strongly smoothes background regions, while preserving transitions. A detectability index is proposed to optimize the prior. The new algorithm has been compared with different reconstruction algorithms such as 3D-OSEM. +. PSF, 3D-OSEM. +. PSF. +. post-filtering and 3D-OSL with a Gauss-Total Variation (GTV) prior. The proposed regularization allows controlling noise, while maintaining good signal recovery; compared to the other algorithms it demonstrates a very good compromise between an improved quantitation and good image quality.

Original languageEnglish
Pages (from-to)445-457
Number of pages13
JournalComputerized Medical Imaging and Graphics
Volume38
Issue number6
DOIs
Publication statusPublished - 2014

Keywords

  • 3-D image reconstruction
  • Image regularization
  • Point spread function
  • Positron emission tomography (PET)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Medicine(all)

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