Optimized Bayes variational regularization prior for 3D PET images

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

doi:10.1016/j.compmedimag.2014.05.004

Optimized Bayes variational regularization prior for 3D PET images

Comput Med Imaging Graph. 2014 Sep;38(6):445-57. doi: 10.1016/j.compmedimag.2014.05.004. Epub 2014 May 14.

Authors

Eugenio Rapisarda¹, Luca Presotto², Elisabetta De Bernardi³, Maria Carla Gilardi⁴, Valentino Bettinardi⁵

Affiliations

¹ IBFM-CNR, Institute for Molecular Bioimaging and Physiology, 20090 Segrate, Italy; Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy; Department of Physics, University of Milano-Bicocca, 20126 Milano, Italy. Electronic address: [email protected].
² Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy; Department of Physics, University of Milano-Bicocca, 20126 Milano, Italy. Electronic address: [email protected].
³ Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy; Department of Health Sciences - Tecnomed Foundation, University of Milano-Bicocca, 20900 Monza, Italy. Electronic address: [email protected].
⁴ IBFM-CNR, Institute for Molecular Bioimaging and Physiology, 20090 Segrate, Italy; Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy; Department of Health Sciences - Tecnomed Foundation, University of Milano-Bicocca, 20900 Monza, Italy. Electronic address: [email protected].
⁵ Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy. Electronic address: [email protected].

PMID: 24958594
DOI: 10.1016/j.compmedimag.2014.05.004

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.

Keywords: 3-D image reconstruction; Image regularization; Point spread function; Positron emission tomography (PET).

MeSH terms

Algorithms*
Bayes Theorem*
Imaging, Three-Dimensional*
Positron-Emission Tomography / methods*