Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging

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dc.contributor.authorVunckx, Ken_AU
dc.contributor.authorAtre, Aen_AU
dc.contributor.authorBaete, Ken_AU
dc.contributor.authorReilhac, Aen_AU
dc.contributor.authorDeroose, CMen_AU
dc.contributor.authorVan Laere, Ken_AU
dc.contributor.authorNuyts, Jen_AU
dc.date.accessioned2020-02-13T00:12:54Zen_AU
dc.date.available2020-02-13T00:12:54Zen_AU
dc.date.issued2012-03-01en_AU
dc.description.abstractIn emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems. © 2012, IEEEen_AU
dc.identifier.citationVunckx, K., Atre, A., Baete, K., Reilhac, A., Deroose, C. M., Van Laere, K., & Nuyts, J. (2012). Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging. IEEE Transactions on Medical imaging, 31(3), 599-612. doi:10.1109/TMI.2011.2173766en_AU
dc.identifier.govdoc8898en_AU
dc.identifier.issn1558-254Xen_AU
dc.identifier.issue3en_AU
dc.identifier.journaltitleIEEE Transactions on Medical imagingen_AU
dc.identifier.pagination599-612en_AU
dc.identifier.urihttps://doi.org/10.1109/TMI.2011.2173766en_AU
dc.identifier.urihttp://apo.ansto.gov.au/dspace/handle/10238/9097en_AU
dc.identifier.volume31en_AU
dc.language.isoenen_AU
dc.publisherInstitute of Electrical and Electronics Engineersen_AU
dc.subjectPositron computed tomographyen_AU
dc.subjectMonte Carlo Methoden_AU
dc.subjectBrainen_AU
dc.subjectMagnetic resonanceen_AU
dc.subjectSignal-to-noise ratioen_AU
dc.subjectAnatomyen_AU
dc.subjectNuclear medicineen_AU
dc.titleEvaluation of three MRI-based anatomical priors for quantitative PET brain imagingen_AU
dc.typeJournal Articleen_AU
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