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dc.contributor.authorGurney-Champion, OJ
dc.contributor.authorKlaassen, R
dc.contributor.authorFroeling, M
dc.contributor.authorBarbieri, S
dc.contributor.authorStoker, J
dc.contributor.authorEngelbrecht, MRW
dc.contributor.authorWilmink, JW
dc.contributor.authorBesselink, MG
dc.contributor.authorBel, A
dc.contributor.authorvan Laarhoven, HWM
dc.contributor.authorNederveen, AJ
dc.date.accessioned2018-04-10T14:50:30Z
dc.date.issued2018-01
dc.identifier.citationPloS one, 2018, 13 (4), pp. e0194590 - ?
dc.identifier.issn1932-6203
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1635
dc.identifier.eissn1932-6203
dc.identifier.doi10.1371/journal.pone.0194590
dc.description.abstractThe intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman's rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.
dc.formatElectronic-eCollection
dc.format.extente0194590 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectPancreatic Neoplasms
dc.subjectImage Interpretation, Computer-Assisted
dc.subjectMagnetic Resonance Imaging
dc.subjectBayes Theorem
dc.subjectProspective Studies
dc.subjectAlgorithms
dc.subjectAged
dc.subjectMiddle Aged
dc.subjectFemale
dc.subjectMale
dc.titleComparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.
dc.typeJournal Article
dcterms.dateAccepted2018-03-06
rioxxterms.versionofrecord10.1371/journal.pone.0194590
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2018-01
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfPloS one
pubs.issue4
pubs.notesNo embargo
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.volume13
pubs.embargo.termsNo embargo
icr.researchteamRadiotherapy Physics Modellingen_US
dc.contributor.icrauthorGurney-Champion, Oliveren


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