Small-animal PET registration method with intrinsic validation designed for large datasets

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dc.contributor.author Pascau González-Garzón, Javier
dc.contributor.author Gispert, J. D.
dc.contributor.author Soto Montenegro, M. L.
dc.contributor.author Rodríguez Ruano, A.
dc.contributor.author García Vázquez, V.
dc.contributor.author Udías, Ángel
dc.contributor.author Vaquero López, Juan José
dc.contributor.author Desco Menéndez, Manuel
dc.date.accessioned 2011-10-04T13:37:59Z
dc.date.available 2011-10-04T13:37:59Z
dc.date.issued 2007
dc.identifier.bibliographicCitation 2007 IEEE Nuclear Science Symposium Conference Record, 2007, vol. 5, p. 3751 - 3753
dc.identifier.isbn 978-1-4244-0922-8
dc.identifier.issn 1082-3654
dc.identifier.uri http://hdl.handle.net/10016/12227
dc.description Proceeding of: 2007 IEEE Nuclear Science Symposium Conference Record (NSS'07), Honolulu, Hawaii, USA, Oct. 27 - Nov. 3, 2007
dc.description.abstract We present a procedure to validate the results of small animal Positron Emission Tomography (PET) image registration by means of consistency measures. Small animal 2-Deoxy-2-[F-18]fluoro-D-glucose (FDG) PET studies do not show the same intensity distribution even for images acquired in similar conditions, as the resulting image is influenced by several variables which are not always completely under control. Because of these difficulties, the results from automatic registration methods have to be visually inspected to detect failures. We propose a method to automate this validation process. Two reference images from the dataset are selected by an expert user avoiding images with poor contrast, animal movement or low quality, and both are co-registered using anatomical landmarks. All the remaining images in the dataset are then registered to every reference with an automatic two-step algorithm based on Mutual Information. The known transformation relating both references allows measuring the registration consistency, which is a good estimator of the accuracy of the alignment process, for every image in the dataset. This value can be used to assess the quality of the registration and therefore detect the incorrect results. We have applied this validation process on a large dataset of 120 FDG-PET rat brain images obtained with a rotating PET scanner. The registration consistency was calculated for every image in the dataset and values below 1.65 mm (PET image resolution) were considered as successful registrations. 116 images were correctly registered with an average error of 0.839 mm, while in four images the proposed method detected a registration failure. Two of these failures were due to very low image quality and these studies were discarded from the study, while the other two were correctly aligned after applying a manual pre-alignment step. Our approach requires minimal user interaction and provides automatic assessment of the registration error, making it unnecessary to visually inspect and check every registration result.
dc.description.sponsorship This work was supported by projects CIBER CB06/01/0079 (Ministerio de Sanidad y Consumo) and CDTEAM (CENIT program, Ministerio de Industria).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.title Small-animal PET registration method with intrinsic validation designed for large datasets
dc.type bookPart
dc.type conferenceObject
dc.relation.publisherversion http://dx.doi.org/10.1109/NSSMIC.2007.4436938
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi 10.1109/NSSMIC.2007.4436938
dc.rights.accessRights openAccess
dc.relation.eventdate Oct. 27 - Nov. 3, 2007
dc.relation.eventplace Honolulu, Hawaii, USA
dc.relation.eventtitle 2007 IEEE Nuclear Science Symposium Conference Record (NSS'07)
dc.identifier.publicationfirstpage 3751
dc.identifier.publicationlastpage 3753
dc.identifier.publicationtitle 2007 IEEE Nuclear Science Symposium Conference Record
dc.identifier.publicationvolume 5
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