Automatic TAC extraction from dynamic cardiac PET imaging using iterative correlation from a population template

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dc.contributor.author Mateos Pérez, José M.
dc.contributor.author Desco Menéndez, Manuel
dc.contributor.author Dae, Michael W.
dc.contributor.author García Villalba, Carmen
dc.contributor.author Cusso Mula, Lorena
dc.contributor.author Vaquero López, Juan José
dc.date.accessioned 2013-07-29T08:54:48Z
dc.date.available 2013-07-29T08:54:48Z
dc.date.issued 2013-08-01
dc.identifier.bibliographicCitation Computer Methods and Programs in Biomedicine, (2013), 111(2), 308-314.
dc.identifier.issn 0169-2607
dc.identifier.uri http://hdl.handle.net/10016/17404
dc.description.abstract This work describes a new iterative method for extracting time-activity curves (TAC) from dynamic imaging studies using a priori information from generic models obtained from TAC templates. Analytical expressions of the TAC templates were derived from TACs obtained by manual segmentation of three 13NH3 pig studies (gold standard). An iterative method for extracting both ventricular and myocardial TACs using models of the curves obtained as an initial template was then implemented and tested. These TACs were extracted from masked and unmasked images; masking was applied to remove the lungs and surrounding non-relevant structures. The resulting TACs were then compared with TACs obtained manually; the results of kinetic analysis were also compared. Extraction of TACs for each region was sensitive to the presence of other organs (e.g., lungs) in the image. Masking the volume of interest noticeably reduces error. The proposed method yields good results in terms of TAC definition and kinetic parameter estimation, even when the initial TAC templates do not accurately match specific tracer kinetics.
dc.description.sponsorship This work is supported by the following grants: RD07/0014/2009, Subprograma RETICS, Ministerio de Ciencia e Innovación. S2009/DPI-1802 (ARTEMIS), Comunidad de Madrid. CEN-20101014, Programa CENIT, CDTI, Ministerio de Ciencia e Innovación. European Commission, EFPIA, INNOVATIVE MEDICINE INITIATIVE (PredDICT-TB project, 115337-1)
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2013 Elsevier Ireland Ltd.
dc.subject.other Positron Emission Tomography (PET)
dc.subject.other Cardiac imaging
dc.subject.other Automatic segmentation
dc.subject.other Kinetic modeling
dc.title Automatic TAC extraction from dynamic cardiac PET imaging using iterative correlation from a population template
dc.type article
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.og/10.1016/j.cmpb.2013.04.010
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi 10.1016/j.cmpb.2013.04.010
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2009/DPI-1802/ARTEMIS
dc.relation.projectID Gobierno de España. RD07/0014/2009
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 308
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 314
dc.identifier.publicationtitle Computer Methods and Programs in Biomedicine
dc.identifier.publicationvolume 111
dc.identifier.uxxi AR/0000013589
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