Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets...

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Main Authors:	Marinelli, Martina, Positano, Vincenzo, Tucci, Francesco, Neglia, Danilo, Landini, Luigi
Format:	Online
Language:	English
Published:	The Scientific World Journal 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3349214/

id	pubmed-3349214
recordtype	oai_dc
spelling	pubmed-33492142012-05-16 Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms Marinelli, Martina Positano, Vincenzo Tucci, Francesco Neglia, Danilo Landini, Luigi Research Article Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets. The Scientific World Journal 2012-04-19 /pmc/articles/PMC3349214/ /pubmed/22593696 http://dx.doi.org/10.1100/2012/567067 Text en Copyright © 2012 Martina Marinelli et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Marinelli, Martina Positano, Vincenzo Tucci, Francesco Neglia, Danilo Landini, Luigi
spellingShingle	Marinelli, Martina Positano, Vincenzo Tucci, Francesco Neglia, Danilo Landini, Luigi Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
author_facet	Marinelli, Martina Positano, Vincenzo Tucci, Francesco Neglia, Danilo Landini, Luigi
author_sort	Marinelli, Martina
title	Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
title_short	Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
title_full	Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
title_fullStr	Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
title_full_unstemmed	Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms
title_sort	automatic pet-ct image registration method based on mutual information and genetic algorithms
description	Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.
publisher	The Scientific World Journal
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3349214/
_version_	1611528641531871232

Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

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