AI Article Synopsis

  • A new modeling method for dynamic contrast-enhanced MRI (DCE-MRI) was developed to separately estimate blood flow and microvascular permeability in individuals.
  • Monte Carlo simulations and experiments on tumor-bearing mice showed that this model provided more accurate results compared to traditional single-bolus approaches.
  • The study demonstrated that significant variations in the behavior of different contrast agents, based on their molecular weight, could effectively characterize tumor vascular status.

Article Abstract

Purpose: To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual.

Methods: Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent.

Results: Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed.

Conclusion: A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.

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http://dx.doi.org/10.1002/mrm.25704DOI Listing

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