Cargando…
Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization
In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which incre...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4247493/ https://www.ncbi.nlm.nih.gov/pubmed/25484635 http://dx.doi.org/10.1007/s11220-014-0097-5 |
_version_ | 1782346649266290688 |
---|---|
author | Kazantsev, Daniil Lionheart, William R. B. Withers, Philip J. Lee, Peter D. |
author_facet | Kazantsev, Daniil Lionheart, William R. B. Withers, Philip J. Lee, Peter D. |
author_sort | Kazantsev, Daniil |
collection | PubMed |
description | In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods. |
format | Online Article Text |
id | pubmed-4247493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-42474932014-12-03 Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization Kazantsev, Daniil Lionheart, William R. B. Withers, Philip J. Lee, Peter D. Sens Imaging Original Paper In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods. Springer US 2014-08-21 2014 /pmc/articles/PMC4247493/ /pubmed/25484635 http://dx.doi.org/10.1007/s11220-014-0097-5 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Original Paper Kazantsev, Daniil Lionheart, William R. B. Withers, Philip J. Lee, Peter D. Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title | Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title_full | Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title_fullStr | Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title_full_unstemmed | Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title_short | Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization |
title_sort | multimodal image reconstruction using supplementary structural information in total variation regularization |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4247493/ https://www.ncbi.nlm.nih.gov/pubmed/25484635 http://dx.doi.org/10.1007/s11220-014-0097-5 |
work_keys_str_mv | AT kazantsevdaniil multimodalimagereconstructionusingsupplementarystructuralinformationintotalvariationregularization AT lionheartwilliamrb multimodalimagereconstructionusingsupplementarystructuralinformationintotalvariationregularization AT withersphilipj multimodalimagereconstructionusingsupplementarystructuralinformationintotalvariationregularization AT leepeterd multimodalimagereconstructionusingsupplementarystructuralinformationintotalvariationregularization |