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Few-View CT Image reconstruction via Least-Squares Methods: assessment and optimization
dc.contributor.author | Chillarón, Mónica | |
dc.contributor.author | Vidal, Vicente | |
dc.contributor.author | Verdu, Gumersindo | |
dc.contributor.author | Quintana-Ortí, Gregorio | |
dc.date.accessioned | 2024-02-12T13:02:41Z | |
dc.date.available | 2024-02-12T13:02:41Z | |
dc.date.issued | 2023-06-30 | |
dc.identifier.citation | Mónica Chillarón Pérez, Vicente E. Vidal, Gumersindo J. Verdú & Gregorio Quintana-Ortí (2024) Few-View CT Image Reconstruction via Least-Squares Methods: Assessment and Optimization, Nuclear Science and Engineering, 198:2, 193-206, DOI: 10.1080/00295639.2023.2199677 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/205815 | |
dc.description.abstract | The use of iterative algebraic methods applied to the reconstruction of Computed Tomography (CT) Medical Images is proliferating to reconstruct high-quality CT images using far fewer views than through analytical methods. This would imply reducing the dose of X-rays applied to patients who require this medical test. Least-squares methods are a promising approach to reconstruct the images with few projections obtaining high quality. In addition, since these techniques involve a high computational load, it is necessary to develop efficient methods that make use of high-performance computing (HPC) tools to accelerate reconstructions. In this paper, three LeastSquares methods are analyzed: LSMB (Least-Squares Model Based), LSQR (Least Squares QR) and LSMR (Least Squares Minimal Residual), to determine whether the LSMB method provides a faster convergence and thus lower computational times. Moreover, a block version of both the LSQR and LSMR methods was implemented. With them, multiple right-hand sides (multiple slices) can be solved at the same time, taking advantage of the parallelism obtained with the implementation of the methods using the Intel Math Kernel Library (MKL). The two implementations are compared in terms of convergence, time, and quality of the images obtained, reducing the number of projections and combining them with a regularization and acceleration technique. The experiments show how the implementations are scalable and obtain images of good quality from a reduced number of views, being the LSQR method better suited for this application. | ca_CA |
dc.format.extent | 23 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Taylor & Francis Group | ca_CA |
dc.relation.isPartOf | Nuclear Science and Engineering, Volume 198, Issue 2 (2024) | ca_CA |
dc.rights | “This is an Accepted Manuscript version of the following article, accepted for publication in Nuclear Science and Engineering. Mónica Chillarón Pérez, Vicente E. Vidal, Gumersindo J. Verdú & Gregorio Quintana-Ortí (2024) Few-View CT Image Reconstruction via Least-Squares Methods: Assessment and Optimization, Nuclear Science and Engineering, 198:2, 193-206, DOI: 10.1080/00295639.2023.2199677. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.” “This is an Accepted Manuscript of an article published by Taylor & Francis in Nuclear Science and Engineering on 2024, available at: https://doi.org/10.1080/00295639.2023.2199677.” | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | ca_CA |
dc.subject | CT | ca_CA |
dc.subject | Computed tomography | ca_CA |
dc.subject | Reconstruction | ca_CA |
dc.subject | Image quality | ca_CA |
dc.subject | Least-squares | ca_CA |
dc.subject | Algebraic methods | ca_CA |
dc.title | Few-View CT Image reconstruction via Least-Squares Methods: assessment and optimization | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1080/00295639.2023.2199677 | |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | ca_CA |
dc.relation.publisherVersion | https://www.tandfonline.com/doi/full/10.1080/00295639.2023.2199677 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | Universitat Politècnica de València | ca_CA |
project.funder.name | Agencia Estatal de Investigación | ca_CA |
project.funder.name | European Union NextGenerationEU/PRTR | ca_CA |
oaire.awardNumber | TED2021-131091B-I00 | ca_CA |
oaire.awardNumber | MCIN/AEI/10.13039/501100011033 | ca_CA |
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Excepto si se señala otra cosa, la licencia del ítem se describe como: “This is an Accepted Manuscript version of the following article, accepted for publication in Nuclear Science and Engineering. Mónica Chillarón Pérez, Vicente E. Vidal, Gumersindo J. Verdú & Gregorio Quintana-Ortí (2024) Few-View CT Image Reconstruction via Least-Squares Methods: Assessment and Optimization, Nuclear Science and Engineering, 198:2, 193-206, DOI: 10.1080/00295639.2023.2199677. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.”
“This is an Accepted Manuscript of an article published by Taylor & Francis in Nuclear Science and Engineering on 2024, available at: https://doi.org/10.1080/00295639.2023.2199677.”