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Quantitative hepatic perfusion modeling using DCE-MRI with sequential breathholds.

Eric M Bultman1, Ethan K Brodsky, Debra E Horng

  • 1Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA.

Journal of Magnetic Resonance Imaging : JMRI
|January 8, 2014
PubMed
Summary

A new method for liver perfusion modeling using interrupted DCE-MRI data is feasible. This technique accurately differentiates healthy liver, cirrhosis, and HCC using quantitative perfusion parameters.

Keywords:
DCE-MRIhepatic perfusion modelinghepatocellular carcinomaquantitative perfusion MRItumor perfusion modeling

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biophysics

Background:

  • Quantitative liver perfusion modeling is crucial for diagnosing and monitoring liver diseases.
  • Dynamic contrast-enhanced MRI (DCE-MRI) is a key imaging modality for assessing liver perfusion.
  • Acquiring uninterrupted DCE-MRI data can be challenging due to patient breath-holding limitations.

Purpose of the Study:

  • To develop and validate a novel mathematical formulation for quantitative liver perfusion modeling using interrupted DCE-MRI data.
  • To assess the feasibility of this method in differentiating between healthy liver, cirrhotic liver, and hepatocellular carcinoma (HCC).

Main Methods:

  • A new mathematical formulation was developed to estimate quantitative perfusion parameters from interrupted DCE-MRI data.
  • A dual-input single-compartment perfusion model was employed, investigating the impact of a second degree-of-freedom in the tissue residue function (TRF) using a Weibull model.
  • Hepatic perfusion parameters were estimated in 12 healthy volunteers and 9 cirrhotic patients with HCC.

Main Results:

  • The Weibull TRF (2 degrees-of-freedom) significantly improved quality-of-fit criteria compared to the exponential TRF (1 degree-of-freedom).
  • Arterial fraction was higher in cirrhotic liver compared to normal liver (P=0.07).
  • Mean transit time and arterial fraction were significantly different between cirrhotic liver and HCC (P=0.01 and P=0.04, respectively).

Conclusions:

  • The developed formulation is feasible for estimating hepatic perfusion parameters from interrupted DCE-MRI data acquired during sequential breath-holds.
  • This method shows potential utility in differentiating between healthy liver, cirrhotic liver, and HCC based on quantitative perfusion parameters.