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dc.contributor.authorBerks, M.
dc.contributor.authorLittle, R. A.
dc.contributor.authorWatson, Y.
dc.contributor.authorCheung, S.
dc.contributor.authorDatta, Anubhav
dc.contributor.authorO'Connor, James P B
dc.contributor.authorScaramuzza, D
dc.contributor.authorParker, G. J. M.
dc.date.accessioned2021-07-19T10:28:43Z
dc.date.available2021-07-19T10:28:43Z
dc.date.issued2021en
dc.identifier.citationBerks M, Little RA, Watson Y, Cheung S, Datta A, O’Connor JPB, et al. A model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma. Magn Reson Med. 2021 May 11;en
dc.identifier.pmid33973674en
dc.identifier.doi10.1002/mrm.28798en
dc.identifier.urihttp://hdl.handle.net/10541/624082
dc.description.abstractPurpose: We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI). Methods: Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes). Results: The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients' non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes. Conclusions: In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1002/mrm.28798en
dc.titleA model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinomaen
dc.typeArticleen
dc.contributor.departmentDivision of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UKen
dc.identifier.journalMagnetic Resonance in Medicineen
dc.description.noteen]
refterms.dateFOA2021-07-26T10:13:05Z


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