Publication:
A novel beam stopper-based approach for scatter correction in digital planar radiography.

dc.contributor.authorSakaltras, N
dc.contributor.authorPena, A
dc.contributor.authorMartinez, C
dc.contributor.authorDesco, Manuel
dc.contributor.authorAbella, M
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es_ES
dc.contributor.funderAgencia Estatal de Investigación (España)es_ES
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)es_ES
dc.contributor.funderUnión Europea. Comisión Europea. NextGenerationEUes_ES
dc.contributor.funderComunidad de Madrid (España)es_ES
dc.contributor.funderBanco Santanderes_ES
dc.contributor.funderInstituto de Salud Carlos IIIes_ES
dc.date.accessioned2024-05-08T11:22:05Z
dc.date.available2024-05-08T11:22:05Z
dc.date.issued2023-05-31
dc.description.abstractX-ray scatter in planar radiography degrades the contrast resolution of the image, thus reducing its diagnostic utility. Antiscatter grids partially block scattered photons at the cost of increasing the dose delivered by two- to four-fold and posing geometrical restrictions that hinder their use for other acquisition settings, such as portable radiography. The few software-based approaches investigated for planar radiography mainly estimate the scatter map from a low-frequency version of the image. We present a novel method for scatter correction in planar imaging based on direct patient measurements. Samples from the shadowed regions of an additional partially obstructed projection acquired with a beam stopper placed between the X-ray source and the patient are used to estimate the scatter map. Evaluation with simulated and real data showed an increase in contrast resolution for both lung and spine and recovery of ground truth values superior to those of three recently proposed methods. Our method avoids the biases of post-processing methods and yields results similar to those for an antiscatter grid while removing geometrical restrictions at around half the radiation dose. It can be used in unconventional imaging techniques, such as portable radiography, where training datasets needed for deep-learning approaches would be very difficult to obtain.es_ES
dc.description.peerreviewedes_ES
dc.description.sponsorshipThis work was supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (DPI2016- 79075-R—AEI/FEDER, UE) and co-funded by the European Regional Development Fund, ‘A way of making Europe’: PID2019‐110369RB‐I00/AEI/10.13039/501100011033 (RADHOR); PDC2021-121656-I00 (MULTIRAD), funded by MCIN/AEI/10.13039/501100011033 and by the European Union ‘NextGenerationEU’/PRTR. Our work was also funded by Comunidad de Madrid: Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (DEEPCT-CM-UC3M) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation); S2017/BMD-3867 RENIM-CM, co‐funded by the European Structural and Investment Fund. Also partially funded by CRUE Universidades, CSIC, and Banco Santander (Fondo Supera Covid19), project RADCOV19 and by Instituto de Salud Carlos III through the projects PT20/00044, co-funded by the European Regional Development Fund “A way to make Europe” and PMPTA22/00121 and PMPTA22/00118, co-funded by the European Union ‘NextGenerationEU’/PRTR. Te CNIC is supported by Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, and the Pro CNIC Foundation.es_ES
dc.format.number1es_ES
dc.format.page8795es_ES
dc.format.volume13es_ES
dc.identifier.citationSci Rep. 2023 May 31;13(1):8795.es_ES
dc.identifier.doi10.1038/s41598-023-32764-5es_ES
dc.identifier.e-issn2045-2322es_ES
dc.identifier.journalScientific reportses_ES
dc.identifier.pubmedID37258545es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/19301
dc.language.isoenges_ES
dc.publisherNature Publishing Groupes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/DPI2016-79075-R—AEI/FEDERes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2019‐110369RB‐I00/AEI/10.13039/501100011033es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PDC2021-121656-I00es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/DEEPCT-CM-UC3Mes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/S2017/BMD-3867/RENIM-CMes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PT20/00044es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PMPTA22/00121es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PMPTA22/00118es_ES
dc.relation.publisherversion10.1038/s41598-023-32764-5es_ES
dc.repisalud.institucionCNICes_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Imagen Avanzadaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleA novel beam stopper-based approach for scatter correction in digital planar radiography.es_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication3d8c68c5-1cf7-41e7-bc20-a44a703ae994
relation.isAuthorOfPublication.latestForDiscovery3d8c68c5-1cf7-41e7-bc20-a44a703ae994

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