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dc.contributor.authorBarbieri, S
dc.contributor.authorGurney-Champion, OJ
dc.contributor.authorKlaassen, R
dc.contributor.authorThoeny, HC
dc.date.accessioned2019-08-08T13:52:51Z
dc.date.issued2020-01
dc.identifier.citationMagnetic resonance in medicine, 2020, 83 (1), pp. 312 - 321
dc.identifier.issn0740-3194
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3314
dc.identifier.eissn1522-2594
dc.identifier.doi10.1002/mrm.27910
dc.description.abstractPurpose This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance.Methods In May 2011, 10 male volunteers (age range, 29-53 years; mean, 37) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T MR scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by 2 readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass correlation coefficients (ICCs) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using coefficients of variation (CVs). The fitting error was calculated based on simulated data, and the average fitting time of each method was recorded.Results DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the 2 readers (ICCs between 50% and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods, but the networks may need to be retrained for different acquisition protocols or imaged anatomical regions.Conclusion DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available for download.
dc.formatPrint-Electronic
dc.format.extent312 - 321
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
dc.titleDeep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.
dc.typeJournal Article
dcterms.dateAccepted2019-06-26
rioxxterms.versionofrecord10.1002/mrm.27910
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
rioxxterms.licenseref.startdate2020-01
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfMagnetic resonance in medicine
pubs.issue1
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.publication-statusPublished
pubs.volume83
pubs.embargo.termsNot known
icr.researchteamRadiotherapy Physics Modellingen_US
dc.contributor.icrauthorGurney-Champion, Oliveren


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