A model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma.
Date
2021-05-11ICR Author
Author
Berks, M
Little, RA
Watson, Y
Cheung, S
Datta, A
O'Connor, JPB
Scaramuzza, D
Parker, GJM
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
gadoxetate
hepatocellular carcinoma
model selection
quantitative DCE-MRI
tracer kinetic modeling
Carcinoma, Hepatocellular
Contrast Media
Gadolinium DTPA
Humans
Liver
Liver Neoplasms
Magnetic Resonance Imaging
Research team
Quant Biomed Imaging
Language
eng
Date accepted
2021-03-19
License start date
2021-05-11
Citation
Magnetic Resonance in Medicine, 2021, 86 (4), pp. 1829 - 1844
Publisher
WILEY