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Compressed Sensing Inspired Image Reconstruction from Overlapped Projections

The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction ap...

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Detalles Bibliográficos
Autores principales: Yang, Lin, Lu, Yang, Wang, Ge
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905705/
https://www.ncbi.nlm.nih.gov/pubmed/20689701
http://dx.doi.org/10.1155/2010/284073
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author Yang, Lin
Lu, Yang
Wang, Ge
author_facet Yang, Lin
Lu, Yang
Wang, Ge
author_sort Yang, Lin
collection PubMed
description The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests.
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spelling pubmed-29057052010-08-05 Compressed Sensing Inspired Image Reconstruction from Overlapped Projections Yang, Lin Lu, Yang Wang, Ge Int J Biomed Imaging Research Article The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests. Hindawi Publishing Corporation 2010 2010-06-22 /pmc/articles/PMC2905705/ /pubmed/20689701 http://dx.doi.org/10.1155/2010/284073 Text en Copyright © 2010 Lin Yang 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.
spellingShingle Research Article
Yang, Lin
Lu, Yang
Wang, Ge
Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title_full Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title_fullStr Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title_full_unstemmed Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title_short Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
title_sort compressed sensing inspired image reconstruction from overlapped projections
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905705/
https://www.ncbi.nlm.nih.gov/pubmed/20689701
http://dx.doi.org/10.1155/2010/284073
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