GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems

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dc.contributor.author Molina Gómez, Claudia de
dc.contributor.author Serrano López, Estefania
dc.contributor.author García Blas, Francisco Javier
dc.contributor.author Carretero Pérez, Jesús
dc.contributor.author Desco Menéndez, Manuel
dc.contributor.author Abella García, Mónica
dc.date.accessioned 2019-11-15T11:30:32Z
dc.date.available 2019-11-15T11:30:32Z
dc.date.issued 2018-05-15
dc.identifier.bibliographicCitation BMC Bioinformatics 19:171 (2018).
dc.identifier.issn 1471-2105
dc.identifier.uri http://hdl.handle.net/10016/29176
dc.description.abstract Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. Results: We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (> 1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. Conclusion: Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48x) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.
dc.description.sponsorship This work has been supported by TEC2013-47270-R, RTC-2014-3028-1, TIN2016-79637-P (Spanish Ministerio de Economia y Competitividad), DPI2016-79075-R (Spanish Ministerio de Economia, Industria y Competitividad), CIBER CB07/09/0031 (Spanish Ministerio de Sanidad y Consumo), RePhrase 644235 (European Commission) and grant FPU14/03875 (Spanish Ministerio de Educacion, Cultura y Deporte).
dc.format.extent 7
dc.language.iso eng
dc.publisher BMC
dc.rights © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other GPU
dc.subject.other Memory management
dc.subject.other Parallel processing
dc.subject.other Iterative reconstruction
dc.subject.other Split Bregman
dc.subject.other Limited-data tomography
dc.subject.other CBCT
dc.title GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi https://doi.org/10.1186/s12859-018-2169-3
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2013-47270-R
dc.relation.projectID Gobierno de España. RTC-2014-3028-1
dc.relation.projectID Gobierno de España. TIN2016-79637-P
dc.relation.projectID Gobierno de España. DPI2016-79075-R
dc.relation.projectID Gobierno de España. CB07/09/003/CIBER
dc.relation.projectID Gobierno de España. FPU14/03875
dc.relation.projectID info:eu-repo/grant/Agreement/EC/H2020/644235/RePhrase
dc.type.version publishedVersion
dc.identifier.publicationtitle BMC Bioinformatics
dc.identifier.publicationvolume 19
dc.identifier.uxxi AR/0000021499
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Ministerio de Educación, Cultura y Deporte (España)
dc.contributor.funder European Commission
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