A model selection framework to quantify microvascular liver function in gadoxetate‐enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma. Issue 4 (11th May 2021)

• Record Type: Journal Article
• Authors: Berks, Michael; Little, Ross A.; Watson, Yvonne; Cheung, Sue; Datta, Anubhav; O'Connor, James P. B.; Scaramuzza, Davide; Parker, Geoff J. M.
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 4(2021)
• Page Start: 1829
• Page End: 1844
• Publication Date: 2021-05-11
• Subjects: gadoxetate -- hepatocellular carcinoma -- model selection -- quantitative DCE‐MRI -- tracer kinetic modeling; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28798
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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.