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Deep correction of breathing-related artifacts in real-time MR-thermometry

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorDENIS DE SENNEVILLE, Baudouin
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorCOUPÉ, Pierrick
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorRIES, Mario
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorFACQ, Laurent
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorMOONEN, Chrit
dc.date.accessioned2024-04-04T02:48:19Z
dc.date.available2024-04-04T02:48:19Z
dc.date.issued2021-01
dc.identifier.issn0895-6111
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191727
dc.description.abstractEnReal-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the- fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance. In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR- thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map. The method’s artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure.
dc.language.isoen
dc.publisherElsevier
dc.subject.enInterventional procedures
dc.subject.enMR-thermometry
dc.subject.enMotion artifacts
dc.subject.enDeep neural network
dc.subject.enReal-time systems
dc.title.enDeep correction of breathing-related artifacts in real-time MR-thermometry
dc.typeArticle de revue
dc.identifier.doi10.1016/j.compmedimag.2020.101834
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
bordeaux.journalComputerized Medical Imaging and Graphics
bordeaux.page101834
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03066089
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03066089v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computerized%20Medical%20Imaging%20and%20Graphics&rft.date=2021-01&rft.spage=101834&rft.epage=101834&rft.eissn=0895-6111&rft.issn=0895-6111&rft.au=DENIS%20DE%20SENNEVILLE,%20Baudouin&COUP%C3%89,%20Pierrick&RIES,%20Mario&FACQ,%20Laurent&MOONEN,%20Chrit&rft.genre=article


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