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An Adaptive Non-Local-Means Filter for Real-Time MR-Thermometry

hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorZACHIU, Cornel
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorRIES, Mario
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorMOONEN, Chrit
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorDENIS DE SENNEVILLE, Baudouin
dc.date.accessioned2024-04-04T03:02:44Z
dc.date.available2024-04-04T03:02:44Z
dc.date.issued2017
dc.identifier.issn0278-0062
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193015
dc.description.abstractEnProton resonance frequency shift-based magnetic resonance thermometry is a currently used technique for monitoring temperature during targeted thermal therapies. However, in order to provide temperature updates with very short latency times, fast MR acquisition schemes are usually employed, which in turn might lead to noisy temperature measurements. This will, in general, have a direct impact on therapy control and endpoint detection. In the current study we address this problem through an improved non-local filtering technique applied on the temperature images. Compared to previous non-local filtering methods, the proposed approach takes into account not only spatial information, but also exploits temporal redundancies. The method is fully automatic and designed to improve the precision of the temperature measurements while at the same time maintaining output accuracy. Additionally, the implementation was optimized in order to ensure real-time availability of the temperature measurements while having a minimal impact on latency. The method was validated in three complementary experiments: a simulation, an ex-vivo and an in-vivo study. Compared to the original non-local means filter and two other previously employed temperature filtering methods, the proposed approach shows considerable improvement in both accuracy and precision of the filtered data. Together with the low computational demands of the numerical scheme, the proposed filtering technique shows great potential for improving temperature measurements during real-time MR thermometry dedicated to targeted thermal therapies.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subject.enImage denoising
dc.subject.enMR-thermometry
dc.subject.enReal-time system
dc.title.enAn Adaptive Non-Local-Means Filter for Real-Time MR-Thermometry
dc.typeArticle de revue
dc.identifier.doi10.1109/TMI.2016.2627221
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
bordeaux.journalIEEE Transactions on Medical Imaging
bordeaux.page904-916
bordeaux.volume36
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-01461332
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01461332v1
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