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A model selection framework to quantify microvascular liver function in gadoxetate‐enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma
Author(s) -
Berks Michael,
Little Ross A.,
Watson Yvonne,
Cheung Sue,
Datta Anubhav,
O’Connor James P. B.,
Scaramuzza Davide,
Parker Geoff J. M.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28798
Subject(s) - voxel , hepatocellular carcinoma , temporal resolution , magnetic resonance imaging , computer science , nuclear medicine , medicine , radiology , physics , quantum mechanics
Purpose 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.