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Andrew J. Reader
Researcher at King's College London
Publications - 235
Citations - 4789
Andrew J. Reader is an academic researcher from King's College London. The author has contributed to research in topics: Iterative reconstruction & Image resolution. The author has an hindex of 34, co-authored 228 publications receiving 4301 citations. Previous affiliations of Andrew J. Reader include University of Manchester & University of Calgary.
Papers
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Journal ArticleDOI
List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations
Frederic Lamare,Frederic Lamare,M J Ledesma Carbayo,T. Cresson,George Kontaxakis,Andres Santos,C. Cheze Le Rest,Andrew J. Reader,D. Visvikis +8 more
TL;DR: Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.
Journal ArticleDOI
Impact of image-space resolution modeling for studies with the high-resolution research tomograph.
F.C. Sureau,Andrew J. Reader,Claude Comtat,Claire Leroy,Maria-Joao Ribeiro,Irène Buvat,Regine Trebossen +6 more
TL;DR: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects and improving spatial resolution in clinical images reconstructed with RM.
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One-pass list-mode EM algorithm for high-resolution 3-D PET image reconstruction into large arrays
Andrew J. Reader,S. Ally,F. Bakatselos,R. Manavaki,R.J. Walledge,A.P. Jeavons,Peter J Julyan,Sha Zhao,D.L. Hastings,Jamal Zweit +9 more
TL;DR: This work presents an algorithm which meets these demands: one-pass list-mode expectation maximization (OPL-EM) algorithm, which operates directly on list- mode data, passes through the data once only, accounts for finite resolution effects in the system model, and can also include regularization.
Journal ArticleDOI
EM algorithm system modeling by image-space techniques for PET reconstruction
TL;DR: The method demonstrates improved image quality in all cases when compared to the conventional FBP and EM methods presently used for clinical data (which do not include resolution modeling).
Journal ArticleDOI
Fast accurate iterative reconstruction for low-statistics positron volume imaging
TL;DR: A fast accurate iterative reconstruction (FAIR) method suitable for low-statistics positron volume imaging has been developed and is shown to offer improved resolution, contrast and noise properties as a direct result of using improved spatial sampling, limited only by hardware specifications.