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Image fusion for dynamic contrast enhanced magnetic resonance imaging
BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of informati...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2004
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC529274/ https://www.ncbi.nlm.nih.gov/pubmed/15494072 http://dx.doi.org/10.1186/1475-925X-3-35 |
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author | Twellmann, Thorsten Saalbach, Axel Gerstung, Olaf Leach, Martin O Nattkemper, Tim W |
author_facet | Twellmann, Thorsten Saalbach, Axel Gerstung, Olaf Leach, Martin O Nattkemper, Tim W |
author_sort | Twellmann, Thorsten |
collection | PubMed |
description | BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation. |
format | Text |
id | pubmed-529274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2004 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-5292742004-11-19 Image fusion for dynamic contrast enhanced magnetic resonance imaging Twellmann, Thorsten Saalbach, Axel Gerstung, Olaf Leach, Martin O Nattkemper, Tim W Biomed Eng Online Research BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation. BioMed Central 2004-10-19 /pmc/articles/PMC529274/ /pubmed/15494072 http://dx.doi.org/10.1186/1475-925X-3-35 Text en Copyright © 2004 Twellmann et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Twellmann, Thorsten Saalbach, Axel Gerstung, Olaf Leach, Martin O Nattkemper, Tim W Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title | Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title_full | Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title_fullStr | Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title_full_unstemmed | Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title_short | Image fusion for dynamic contrast enhanced magnetic resonance imaging |
title_sort | image fusion for dynamic contrast enhanced magnetic resonance imaging |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC529274/ https://www.ncbi.nlm.nih.gov/pubmed/15494072 http://dx.doi.org/10.1186/1475-925X-3-35 |
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