Multi-compartmental modeling for Gd-EOB-DTPA using sparse human DCE-MRI data

TL;DR

This paper develops a first-principles multi-compartmental model for Gd-EOB-DTPA in liver DCE-MRI, demonstrating its ability to fit low-resolution data and quantify liver function more effectively than existing models.

Contribution

The authors derived a novel three-compartment model from first principles and showed it outperforms the Tofts model in fitting sparse human liver DCE-MRI data.

Findings

The two-compartment model is equivalent to the Tofts model.

The three-compartment model fits all Gd-EOB-DTPA data, unlike the Tofts model.

The model's parameters can quantify liver function.

Abstract

Purpose: To derive kinetic equations for multi-compartmental contrast agent distribution from first principles and apply it to two and three compartments for Gd-EOB-DTPA using low time resolution human liver DCE-MRI data. Methods: The continuity and diffusion equation were combined and used to derive a general form for differential equations governing multi-compartmental particle exchange. They were applied to two (equivalent to the Tofts model) and three compartments. Both models were fit to human DCE-MRI data with low temporal resolution and three compartment model's parameters' implications are discussed. Results: The model derived for two compartments is shown to be equivalent with the Tofts model. Using reasonable biological and physical assumptions an analytical solution for the three compartment model is obtained. The three compartment model was able to fit all Gd-EOB-DTPA DCE-MRI data whereas the Tofts model did not. Differences were increased for cases with large as well as rapid uptake of contrast. Conclusion: We demonstrated the ability to fit sparse Gd-EOB-DTPA DCE-MRI data using a three compartment model derived from first principles whose parameters can be used to help quantify overall and regional liver function.