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Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Biomedical Imaging and Instrumentation Groupes
dc.contributor.authorSerrano López, Estefania
dc.contributor.authorGarcía Blas, Francisco Javier
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.authorDesco Menéndez, Manuel
dc.contributor.authorAbella García, Mónica
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2021-11-26T09:58:20Z
dc.date.available2022-05-31T23:00:07Z
dc.date.issued2020-05-01
dc.description.abstractOne of the latest trends in Computed Tomography (CT) is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artifacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative algorithms to enhance image quality. Most approaches are built around two main operators, backprojection and projection, which are computationally expensive. In this work, we present an implementation of those operators for iterative reconstruction methods exploiting the Big Data paradigm. We define an architecture based on Apache Spark that supports both Graphical Processing Units (GPU) and CPU-based architectures. The aforementioned are parallelized using a partitioning scheme based on the division of the volume and irregular data structures in order to reduce the cost of communication and computation of the final images. Our solution accelerates the execution of the two most computational expensive components with Apache Spark, improving the programming experience of new iterative reconstruction algorithms and the maintainability of the source code increasing the level of abstraction for non-experienced high performance programmers. Through an experimental evaluation, we show that we can obtain results up to 10 faster for projection and 21 faster for backprojection when using a GPU-based cluster compared to a traditional multi-core version. Although a linear speed up was not reached, the proposed approach can be a good alternative for porting previous medical image reconstruction applications already implemented in C/C++ or even with CUDA or OpenCL programming models. Our solution enables the automatic detection of the GPU devices and execution on CPU and GPU tasks at the same time under the same system, using all the available resources.en
dc.description.sponsorshipThis work was supported by the NIH, United States under Grant R01-HL-098686 and Grant U01 EB018753, the Spanish Ministerio de Economia y Competitividad (projects TEC2013-47270-R, RTC-2014-3028 and TIN2016-79637-P), the Spanish Ministerio de Educacion (grant FPU14/03875), the Spanish Ministerio de Ciencia, Innovacion y Universidades (Instituto de Salud Carlos III, project DTS17/00122; Agencia Estatal de Investigacion, project DPI2016-79075-R-AEI/FEDER, UE), co-funded by European Regional Development Fund (ERDF), ‘‘A way of making Europe’’. The CNIC is supported by the Ministerio de Ciencia, Spain, Innovacion y Universidades, Spain and the Pro CNIC Foundation, Spain, and is a Severo Ochoa Center of Excellence, Spain (SEV-2015-0505). Finally, this research was partially supported by Madrid regional Government, Spain under the grant ’’Convergencia Big data-Hpc: de los sensores a las Aplicaciones. (CABAHLA-CM)’’. Ref: S2018/TCS-4423.en
dc.identifier.bibliographicCitationSerrano E., García-Blás, J., Carretero, J., Desco, M., Abella, M. (2020). Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworkse. Future Generation Computer Systems, 106, pp. 534-544. https://doi.org/10.1016/j.future.2019.12.042en
dc.identifier.doihttps://doi.org/10.1016/j.future.2019.12.042
dc.identifier.issn1872-7115
dc.identifier.publicationfirstpage534
dc.identifier.publicationlastpage544
dc.identifier.publicationtitleFuture generation computer systemsen
dc.identifier.publicationvolume106
dc.identifier.urihttps://hdl.handle.net/10016/33698
dc.identifier.uxxiAR/0000025712
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TEC2013-47270-Res
dc.relation.projectIDGobierno de España. RTC-2014-3028-1es
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4423es
dc.relation.projectIDGobierno de España. TIN2016-79637-Pes
dc.relation.projectIDGobierno de España. FPU14/03875es
dc.rights© 2020 Elsevier B.V.
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherapache sparken
dc.subject.othercomputed tomographyen
dc.subject.othergpuen
dc.subject.otheriterative reconstruction algorithmsen
dc.subject.othermedical image processingen
dc.titleAccelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworksen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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