Literature DB >> 27157872

Advances in PET Image Reconstruction.

Andrew J Reader1, Habib Zaidi2.   

Abstract

Until recently, the most widely used methods for image reconstruction were direct analytic techniques. Iterative techniques, although computationally much more intensive, produce improved images (principally arising from more accurate modeling of the acquired projection data), enabling these techniques to replace analytic techniques not only in research settings but also in the clinic. This article offers an overview of image reconstruction theory and algorithms for PET, with a particular emphasis on statistical iterative reconstruction techniques. Future directions for image reconstruction in PET are considered, which concern mainly improving the modeling of the data acquisition process and task-specific specification of the parameters to be estimated in image reconstruction.
Copyright © 2007 Elsevier Inc. All rights reserved.

Year:  2008        PMID: 27157872     DOI: 10.1016/j.cpet.2007.08.001

Source DB:  PubMed          Journal:  PET Clin        ISSN: 1556-8598


  10 in total

1.  A sinogram warping strategy for pre-reconstruction 4D PET optimization.

Authors:  Chiara Gianoli; Marco Riboldi; Giulia Fontana; Christopher Kurz; Katia Parodi; Guido Baroni
Journal:  Med Biol Eng Comput       Date:  2015-07-01       Impact factor: 2.602

2.  3.5D dynamic PET image reconstruction incorporating kinetics-based clusters.

Authors:  Lijun Lu; Nicolas A Karakatsanis; Jing Tang; Wufan Chen; Arman Rahmim
Journal:  Phys Med Biol       Date:  2012-08-07       Impact factor: 3.609

3.  Pre-computed system matrix calculation based on a piece-wise method for PET.

Authors:  Abdella M Ahmed; Yohei Kikuchi; Shigeo Matsuyama; Atsuki Terakawa; Sodai Takyu; Hiroyuki Sugai; Keizo Ishii
Journal:  Radiol Phys Technol       Date:  2014-09-26

Review 4.  Advances in PET/MR instrumentation and image reconstruction.

Authors:  Jorge Cabello; Sibylle I Ziegler
Journal:  Br J Radiol       Date:  2016-07-22       Impact factor: 3.039

Review 5.  Towards enhanced PET quantification in clinical oncology.

Authors:  Habib Zaidi; Nicolas Karakatsanis
Journal:  Br J Radiol       Date:  2017-11-22       Impact factor: 3.039

Review 6.  Artificial intelligence in molecular imaging.

Authors:  Edward H Herskovits
Journal:  Ann Transl Med       Date:  2021-05

7.  Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space.

Authors:  Amirhossein Sanaat; Hossein Arabi; Ismini Mainta; Valentina Garibotto; Habib Zaidi
Journal:  J Nucl Med       Date:  2020-01-10       Impact factor: 11.082

8.  Non-invasive in vivo imaging of acute thrombosis: development of a novel factor XIIIa radiotracer.

Authors:  Jack P M Andrews; Christophe Portal; Tashfeen Walton; Mark G Macaskill; Patrick W F Hadoke; Carlos Alcaide Corral; Christophe Lucatelli; Simon Wilson; Ian Wilson; Gillian MacNaught; Marc R Dweck; David E Newby; Adriana A S Tavares
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2020-06-01       Impact factor: 6.875

9.  Penalized-Likelihood PET Image Reconstruction Using Similarity-Driven Median Regularization.

Authors:  Xue Ren; Ji Eun Jung; Wen Zhu; Soo-Jin Lee
Journal:  Tomography       Date:  2022-01-06

10.  Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

Authors:  Baderaldeen A Altazi; Geoffrey G Zhang; Daniel C Fernandez; Michael E Montejo; Dylan Hunt; Joan Werner; Matthew C Biagioli; Eduardo G Moros
Journal:  J Appl Clin Med Phys       Date:  2017-09-11       Impact factor: 2.102

  10 in total

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