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Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging.

dc.rights.licenseopenen_US
hal.structure.identifierIBIO - Plateforme de bio-imagerie en sciences médicales
dc.contributor.authorYAMAMOTO, Takayuki
dc.contributor.authorLACHERET, C
hal.structure.identifierIBIO - Plateforme de bio-imagerie en sciences médicales
dc.contributor.authorFUKUTOMI, Hikaru
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorKAMRAOUI, Reda Abdellah
hal.structure.identifierIBIO - Plateforme de bio-imagerie en sciences médicales
dc.contributor.authorDENAT, L
dc.contributor.authorZHANG, B
dc.contributor.authorPREVOST, V
dc.contributor.authorZHANG, L
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorRUET, Aurelie
dc.contributor.authorTRIAIRE, B
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorDOUSSET, Vincent
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorCOUPE, Pierrick
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorTOURDIAS, Thomas
dc.date.accessioned2022-12-12T15:35:06Z
dc.date.available2022-12-12T15:35:06Z
dc.date.issued2022-07-28
dc.identifier.issn1936-959Xen_US
dc.identifier.otherhttp://dx.doi.org/10.3174/ajnr.A7589en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/170578
dc.description.abstractEnAccurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
dc.description.sponsorshipTranslational Research and Advanced Imaging Laboratory - ANR-10-LABX-0057en_US
dc.language.isoENen_US
dc.title.enValidation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging.
dc.title.alternativeAJNR Am J Neuroradiolen_US
dc.typeArticle de revueen_US
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]en_US
dc.identifier.pubmed35902124en_US
bordeaux.journalAmerican Journal of Neuroradiologyen_US
bordeaux.page1099-1106en_US
bordeaux.volume43en_US
bordeaux.hal.laboratoriesNeurocentre Magendie - U1215en_US
bordeaux.issue8en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionCHU de Bordeauxen_US
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.teamRelations glie-neuroneen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDFondation pour l'Aide à la Recherche sur la Sclérose en Plaquesen_US
bordeaux.import.sourcepubmed
hal.identifierhal-03895141
hal.version1
hal.date.transferred2022-12-12T15:35:23Z
hal.exporttrue
workflow.import.sourcepubmed
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=American%20Journal%20of%20Neuroradiology&rft.date=2022-07-28&rft.volume=43&rft.issue=8&rft.spage=1099-1106&rft.epage=1099-1106&rft.eissn=1936-959X&rft.issn=1936-959X&rft.au=YAMAMOTO,%20Takayuki&LACHERET,%20C&FUKUTOMI,%20Hikaru&KAMRAOUI,%20Reda%20Abdellah&DENAT,%20L&rft.genre=article


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