A Multiresolution Approach to Discrete Tomography Using DART
In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more ac...
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          | Published in | PloS one Vol. 9; no. 9; p. e106090 | 
|---|---|
| Main Authors | , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        United States
          Public Library of Science
    
        05.09.2014
     Public Library of Science (PLoS)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1932-6203 1932-6203  | 
| DOI | 10.1371/journal.pone.0106090 | 
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| Abstract | In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible. | 
    
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| AbstractList | In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible. In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.  | 
    
| Author | Sijbers, Jan Dabravolski, Andrei Batenburg, Kees Joost  | 
    
| AuthorAffiliation | Banner Alzheimer's Institute, United States of America 1 iMinds-Vision lab, University of Antwerp, Antwerp, Belgium 2 Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands 3 Mathematical Institute, Leiden University, Leiden, The Netherlands  | 
    
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| Author_xml | – sequence: 1 givenname: Andrei surname: Dabravolski fullname: Dabravolski, Andrei – sequence: 2 givenname: Kees Joost surname: Batenburg fullname: Batenburg, Kees Joost – sequence: 3 givenname: Jan surname: Sijbers fullname: Sijbers, Jan  | 
    
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25192003$$D View this record in MEDLINE/PubMed | 
    
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| Cites_doi | 10.1007/978-3-319-07148-0_20 10.1016/j.gmod.2011.06.006 10.3233/FI-2013-861 10.1109/TMI.1982.4307572 10.1016/j.jsb.2011.07.017 10.1109/TIP.2013.2297025 10.1109/TIP.2011.2131661 10.1007/978-1-4612-1568-4 10.1109/TMI.2008.923696 10.1007/s11207-010-9536-1 10.1016/j.ultramic.2012.01.015 10.3233/XST-130408 10.1088/1742-6596/463/1/012012 10.1109/TIP.2012.2206042 10.1117/12.679497 10.1038/nmat2406 10.3233/XST-2012-0314 10.1117/12.652603 10.1179/1743280413Y.0000000023 10.1109/TIP.2011.2114894 10.1016/j.ultramic.2009.01.009 10.1109/TSMC.1979.4310076  | 
    
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| Copyright | 2014 Dabravolski et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Dabravolski et al 2014 Dabravolski et al  | 
    
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: AD KJB JS. Performed the experiments: AD. Analyzed the data: AD. Contributed to the writing of the manuscript: AD KJB JS.  | 
    
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| Title | A Multiresolution Approach to Discrete Tomography Using DART | 
    
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