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New method for correcting beam-hardening artifacts in CT images via deep learning

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Biomedical Imaging and Instrumentation Groupes
dc.contributor.authorMartínez Sánchez, Cristóbal
dc.contributor.authorFernandez Del Cerro, Carlos
dc.contributor.authorDesco Menéndez, Manuel
dc.contributor.authorAbella García, Mónica
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-05-24T15:23:41Z
dc.date.available2023-05-24T15:23:41Z
dc.date.issued2021-07-19
dc.descriptionProceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 19-23 July 2021, Leuven, Belgium.en
dc.description.abstractBeam-hardening is the increase of the mean energy of an X-ray beam as it traverses a material. This effect produces two artifacts in the reconstructed image: cupping in homogeneous regions and dark bands among dense areas in heterogeneous regions. The correction methods proposed in the literature can be divided into post-processing and iterative methods. The former methods usually need a bone segmentation, which can fail in low-dose acquisitions, while the latter methods need several projections and reconstructions, increasing the computation time. In this work, we propose a new method for correcting the beamhardening artifacts in CT based on deep learning. A U-Net network was trained with rodent data for two scenarios: standard and low-dose. Results in an independent rodent study showed an optimum correction for both scenarios, similar to that of iterative approaches, but with a reduction of computational time of two orders of magnitude.en
dc.description.sponsorshipThis work has been supported by project "DEEPCT-CMUC3M", funded by the call "Programa de apoyo a la realización de proyectos interdisciplinares de I+D para jóvenes investigadores de la UC3M 2019-2020, Convenio Plurianual CAM - UC3M" and project "RADCOV19", funded by CRUE Universidades, CSIC and Banco Santander (Fondo Supera). The CNIC is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).en
dc.format.extent5
dc.identifier.bibliographicCitationC. Martínez, C. F. Del Cerro, M. Desco & M. Abella (19-23 July 2021). New method for correcting beam-hardening artifacts in CT images via deep learning [poster]. Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, Leuven, Belgium, pp. 188-192.en
dc.identifier.doihttps://doi.org/10.48550/arXiv.2110.04143
dc.identifier.publicationfirstpage188
dc.identifier.publicationlastpage192
dc.identifier.publicationtitleProceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicineen
dc.identifier.urihttps://hdl.handle.net/10016/37354
dc.identifier.uxxiCC/0000034354
dc.language.isoeng
dc.publisherCornell Universityen
dc.relation.eventdate2021-07-19
dc.relation.eventplaceBÉLGICAes
dc.relation.eventtitle16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine (Fully3D 2021)en
dc.relation.projectIDComunidad de Madrid. DEEPCT-CM-UC3Mes
dc.relation.projectIDGobierno de España. SEV-2015-0505es
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.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaElectrónicaes
dc.subject.ecienciaMedicinaes
dc.titleNew method for correcting beam-hardening artifacts in CT images via deep learningen
dc.typeconference poster*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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