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pmid: 31944935
pmc: PMC7273977
Supporting data and MATLAB code for the paper: A. J. Reader and S. Ellis, "Bootstrap-Optimised Regularised Image Reconstruction for Emission Tomography," in IEEE Transactions on Medical Imaging (2020) DOI: 10.1109/TMI.2019.2956878 Instructions for use (tested on MATLAB R2017a): - unzip the file bootstrap_optimised_PET_image_reconstruction.zip Dependencies - add the utils directory to your path before running the scripts. Figures - there is a directory for each figure, not including those figures which do not contain experimental results. Each directory contains a .m script file and a .mat data file. Running the .m file produces the figure roughly as it appears in the manuscript. Independent exploration of the data can be performed if desired. Sample code - Running the example.m file will perform example 2D reconstructions with MLEM, bootstrap optimised guided quadratic MAPEM, and bootstrap optimised unweighted quadratic MAPEM. The reconstruction code is contained in the @reconClass folder. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [EP/M020142/1]; and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z].
inverse problems, Phantoms, Imaging, hyperparameter selection, Article, 004, 620, emission tomography, Positron-Emission Tomography, Image reconstruction, regularisation, Image Processing, Computer-Assisted, Tomography, X-Ray Computed, Algorithms, bootstrap methods
inverse problems, Phantoms, Imaging, hyperparameter selection, Article, 004, 620, emission tomography, Positron-Emission Tomography, Image reconstruction, regularisation, Image Processing, Computer-Assisted, Tomography, X-Ray Computed, Algorithms, bootstrap methods
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