Incorporating contrast agent diffusion into the analysis of DCE-MRI data

Area of Science:

• Medical Imaging
• Pharmacokinetics
• Biophysics

Background:

• Standard pharmacokinetic models for DCE-MRI (e.g., Tofts model) do not account for contrast agent diffusion.
• This limitation leads to inaccurate K(trans) estimations, with a 43% mean absolute relative difference in simulated data with distinct perfusion regions.
• Unphysical estimations of v(e) are observed in tumor cores using standard models.

Purpose of the Study:

• To introduce a diffusion-perfusion (DP) model that integrates contrast agent diffusion into standard pharmacokinetic models.
• To evaluate the accuracy of the DP model in retrieving pharmacokinetic parameters (K(trans), v(e)) from simulated and experimental DCE-MRI data.
• To assess the DP model's ability to provide physically realistic parameter estimations in tumor tissues.

Main Methods:

• Development of a two-compartment diffusion-perfusion (DP) model incorporating low molecular weight contrast agent diffusion.
• Validation using realistic simulated DCE-MRI data with defined perfusion gradients.
• Application to DCE-MRI data from mouse adenocarcinoma xenografts exhibiting contrast agent diffusion.

Main Results:

• The DP model achieved significantly improved accuracy for K(trans) and v(e) (16% and 17% mean ARD, respectively) on simulated data.
• The DP model provided physically realistic v(e) values (0 < v(e) < 1) throughout the tumor tissue in xenograft data.
• K(trans) distributions from the DP model better reflected the observed perfusion heterogeneity in mouse tumors.

Conclusions:

• The proposed diffusion-perfusion (DP) model enhances the accuracy and physical realism of DCE-MRI pharmacokinetic analysis.
• Incorporating contrast agent diffusion is crucial for precise K(trans) and v(e) estimation, especially in heterogeneous tissues.
• The DP model offers a more robust approach for characterizing tumor vascularity and perfusion dynamics.