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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kazantsev, Daniil, Lionheart, William R. B., Withers, Philip J., Lee, Peter D.
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