Abstract
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). 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). 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. 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.
Highlights
| INTRODUCTIONGadoxetate disodium (Eovist or Primovist, Bayer Healthcare, Berlin, Germany) is a gadolinium-b ased hepatobiliary contrast agent taken up by hepatocytes and excreted by the biliary pathway, allowing more direct measurement of liver function than standard extracellular contrast agents
Other tracer kinetic modeling studies of gadoxetate include initial attempts to estimate intracellular uptake fraction as a marker of liver function[27,28]; these were limited by using models comprising a single-input blood supply, which further failed to account for active transport of gadoxetate into the intracellular space
Analysis of the in vivo data conforms to our initial hypotheses and reflects the results of the Monte Carlo simulations: dynamic time series in healthy liver tissue are best fitted by models that account for active transport of gadoxetate by hepatocytes from a well-m ixed arterial and venous blood supply
Summary
Gadoxetate disodium (Eovist or Primovist, Bayer Healthcare, Berlin, Germany) is a gadolinium-b ased hepatobiliary contrast agent taken up by hepatocytes and excreted by the biliary pathway, allowing more direct measurement of liver function than standard extracellular contrast agents. Other tracer kinetic modeling studies of gadoxetate include initial attempts to estimate intracellular uptake fraction as a marker of liver function[27,28]; these were limited by using models comprising a single-input blood supply, which further failed to account for active transport of gadoxetate into the intracellular space These shortcomings were resolved in a dual-input, uptake model[29,30] that provided significantly better fit than the single-compartment model. Computing parameter estimates over a whole ROI risks including voxels that do not meet a model’s physiological assumptions, potentially leading to unreliable estimates and misleading interpretations This suggests voxel-w ise model selection may be a more suitable method for analyzing the data.[15,32] Common co-morbidities associated with HCC provide additional complications, with patients having a range of liver function and signs of cirrhosis, fibrosis and splenomegaly which may further confound model assumptions if analysis is extended to the non-tumorous liver. We test our hypothesizes on 2 in vivo datasets: the healthy volunteers presented in Ref. [31] and a new dataset of 10 patients with HCC, showing links between model selection and patient co-morbidities, determining the limitations of the data and using these to make recommendations for designing further patient studies
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