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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

Extended graphical model for analysis of dynamic contrast-enhanced MRI.

Huijun Chen¹, Feiyu Li, Xihai Zhao

¹Department of Radiology, University of Washington, Seattle, Washington 98109, USA.

Magnetic Resonance in Medicine

|March 12, 2011

Summary

A new extended graphical model improves kinetic analysis in dynamic contrast-enhanced MRI (DCE-MRI). It offers more accurate physiological parameter estimates (Ktrans, vp) with reduced bias and noise sensitivity, enabling shorter scan times.

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Area of Science:

Medical Imaging
Biophysics
Mathematical Modeling

Background:

Kinetic analysis using mathematical models is crucial for quantifying physiological parameters in medical imaging techniques like CT, PET, and DCE-MRI.
The modified Kety/Tofts and Patlak models are commonly used for analyzing DCE-MRI data in various diseases, including cancer and ischemia.

Purpose of the Study:

To derive and evaluate a novel extended graphical model as an intermediate between the modified Kety/Tofts and Patlak models.
To compare the performance of the new model against existing models using simulations and in vivo experiments.

Main Methods:

Mathematical expansion of the modified Kety/Tofts model to derive the extended graphical model.
Simulations and an in vivo DCE-MRI experiment of carotid atherosclerosis were conducted for model comparison.

Main Results:

The extended graphical model demonstrated lower noise sensitivity and more accurate estimates of Ktrans and vp compared to the modified Kety/Tofts model, especially for shorter acquisition durations (<100-300 s).
The new model exhibited significantly reduced bias (74.4-99.8%) in Ktrans estimates compared to the Patlak model.

Conclusions:

The extended graphical model offers a promising alternative for kinetic modeling in DCE-MRI.
This model may enable accurate kinetic parameter estimation (Ktrans, vp) with reduced data acquisition times, enhancing clinical applicability.