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hal.structure.identifierInstitut de Mathématiques de Marseille [I2M]
dc.contributor.authorMELOT, Clothilde
hal.structure.identifierCentre de Physique des Particules de Marseille [CPPM]
dc.contributor.authorBOURSIER, Yannick
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorAUJOL, Jean-François
hal.structure.identifierInstitut de Mathématiques de Marseille [I2M]
dc.contributor.authorANTHOINE, Sandrine
dc.date.accessioned2024-04-04T02:26:29Z
dc.date.available2024-04-04T02:26:29Z
dc.date.issued2012-11
dc.identifier.issn1930-8337
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189954
dc.description.abstractEnCone-Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) are two complementary medical imaging modalities providing respectively anatomic and metabolic information on a patient. In the context of public health, one must address the problem of dose reduction of the potentially harmful quantities related to each exam protocol : X-rays for CBCT and radiotracer for PET. Two demonstrators based on a technological breakthrough (acquisition devices work in photon-counting mode) have been developed. It turns out that in this low-dose context, i.e. for low intensity signals acquired by photon counting devices, noise should not be approximated anymore by a Gaussian distribution, but is following a Poisson distribution. We investigate in this paper the two related tomographic reconstruction problems. We formulate separately the CBCT and the PET problems in two general frameworks that encompass the physics of the acquisition devices and the specific discretization of the object to reconstruct. We propose various fast numerical schemes based on proximal methods to compute the solution of each problem. In particular, we show that primal-dual approaches are well suited in the PET case when considering non differentiable regularizations such as Total Variation. Experiments on numerical simulations and real data are in favor of the proposed algorithms when compared with well-established methods.
dc.description.sponsorshipAdaptivité pour la représentation des images naturelles et des textures - ANR-08-EMER-0009
dc.language.isoen
dc.publisherAIMS American Institute of Mathematical Sciences
dc.subject.enwavelet $\ell_1$-regularization
dc.subject.enTomography
dc.subject.entotal variation
dc.subject.enPoisson noise
dc.subject.enproximal methods
dc.subject.enPET
dc.subject.enCT
dc.title.enSome proximal methods for Poisson intensity CBCT and PET
dc.typeArticle de revue
dc.identifier.doi10.3934/ipi.2012.6.565
dc.subject.halInformatique [cs]/Traitement des images
bordeaux.journalInverse Problems and Imaging
bordeaux.pagep. 565-598
bordeaux.volume6
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue4
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00640215
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00640215v1
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