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Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models


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dc.creator Llera, Alberto es
dc.creator Huertas Fernández, Ismael es
dc.creator Mir Rivera, Pablo es
dc.creator Beckmann, Christian F. es 2018-08-20T11:49:33Z 2018-08-20T11:49:33Z 2018-07-09
dc.identifier.citation Llera, A., Huertas Fernández, I., Mir Rivera, P. y Beckmann, C.F. (2018). Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models. Molecular Imaging and Biology
dc.identifier.issn 1536-1632 es
dc.identifier.issn 1860-2002 es
dc.description.abstract PURPOSE: Differences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation. PROCEDURES: The normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients. RESULTS: Raw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0, 1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables. CONCLUSION: The GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images. es
dc.description.sponsorship Instituto de Salud Carlos III FI14/00497 MV15/00034 es
dc.description.sponsorship Fondo Europeo de Desarrollo Regional FI14/00497 MV15/00034 es
dc.description.sponsorship ISCIII-FEDER PI16/01575 es
dc.description.sponsorship Wellcome Trust UK Strategic Award 098369/Z/12/Z es
dc.description.sponsorship Netherland Organization for Scientific Research NWO-Vidi 864-12-003 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Springer es
dc.relation.ispartof Molecular Imaging and Biology
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Dopamine transporter es
dc.subject Gamma distribution es
dc.subject Intensity normalization es
dc.subject Multicenter studies es
dc.subject PPMI es
dc.subject SPECT es
dc.title Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Instituto de Biomedicina de Sevilla (IBIS) es
dc.relation.projectID FI14/00497 es
dc.relation.projectID MV15/00034 es
dc.relation.projectID PI16/01575 es
dc.relation.projectID 098369/Z/12/Z es
dc.relation.projectID NWO-Vidi 864-12-003 es
dc.relation.publisherversion es
dc.identifier.doi 10.1007/s11307-018-1217-8 es
dc.journaltitle Molecular Imaging and Biology es
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