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dc.contributor.authorLopez-Montes, Alejandro
dc.contributor.authorGalve, Pablo
dc.contributor.authorUdias, Jose Manuel
dc.contributor.authorCal-Gonzalez, Jacobo
dc.contributor.authorVaquero, Juan Jose
dc.contributor.authorDesco, Manuel 
dc.contributor.authorHerraiz, Joaquin L
dc.date.accessioned2021-06-22T07:00:56Z
dc.date.available2021-06-22T07:00:56Z
dc.date.issued2020-02
dc.identifier.citationAppl Sci. 2020; 10(8):18es_ES
dc.identifier.issn2076-3417es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/13171
dc.description.abstractReal-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.es_ES
dc.description.sponsorshipPart of the calculations in this work were performed in the "Cluster de Calculo para Tecnicas Fisicas" funded in part by UCM and in part by UE Regional Funds. We acknowledge the support from the Spanish Government (FPA2015-65035-P, RTC-2015-3772-2, and RTI2018-095800-A-I00), Comunidad de Madrid (S2013/MIT-3024 TOPUS-CM, B2017/BMD-3888 PRONTO-CM), and European Regional Funds. This work was also supported by the EU's H2020 under MediNet, a Networking Activity of ENSAR-2 (grant agreement 654002), and by a NIH R01 CA215700-2 grant. The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).es_ES
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI) es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleReal-Time 3D PET Image with Pseudoinverse Reconstructiones_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.format.volume10es_ES
dc.format.number8es_ES
dc.format.page2829es_ES
dc.identifier.doi10.3390/app10082829es_ES
dc.contributor.funderComplutense University of Madrid (España)
dc.contributor.funderEuropean Regional Development Fund 
dc.contributor.funderGovernment of Spain
dc.contributor.funderComunidad de Madrid 
dc.contributor.funderMedinet
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)
dc.contributor.funderFundación ProCNIC
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.3390/app10082829es_ES
dc.identifier.journalApplied Scienceses_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Imagen Avanzadaes_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/FPA2015-65035-Pes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/RTI2018-095800-A-I00es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/S2013/MIT-3024 TOPUS-CM,es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/B2017/BMD-3888 PRONTO-CMes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/SEV-2015-0505es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/654002es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/NIHR01CA215700-2es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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