DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility. Academic Article uri icon

Overview

abstract

  • PURPOSE: To evaluate the effect of different methods to convert magnetic resonance (MR) signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model-free and modeled hepatic perfusion parameters measured with dynamic contrast-enhanced (DCE)-MRI. MATERIALS AND METHODS: In this Institutional Review Board (IRB)-approved prospective study, 23 DCE-MRI examinations of the liver were performed on 17 patients. SI was converted to [Gd] using linearity vs. nonlinearity assumptions (using spoiled gradient recalled echo [SPGR] signal equations). The [Gd] vs. time curves were analyzed using model-free parameters and a dual-input single compartment model. Perfusion parameters obtained with the two conversion methods were compared using paired Wilcoxon test. Test-retest and interobserver reproducibility of perfusion parameters were assessed in six patients. RESULTS: There were significant differences between the two conversion methods for the following parameters: AUC60 (area under the curve at 60 s, P < 0.001), peak gadolinium concentration (Cpeak, P < 0.001), upslope (P < 0.001), Fp (portal flow, P = 0.04), total hepatic flow (Ft, P = 0.007), and MTT (mean transit time, P < 0.001). Our preliminary results showed acceptable to good reproducibility for all model-free parameters for both methods (mean coefficient of variation [CV] range, 11.87-23.7%), except for upslope (CV = 37%). Among modeled parameters, DV (distribution volume) had CV <22% with both methods, PV and MTT showed CV <21% and <29% using SPGR equations, respectively. Other modeled parameters had CV >30% with both methods. CONCLUSION: Linearity assumption is acceptable for quantification of model-free hepatic perfusion parameters while the use of SPGR equations and T1 mapping may be recommended for the quantification of modeled hepatic perfusion parameters.

publication date

  • November 4, 2013

Research

keywords

  • Algorithms
  • Hepatitis C
  • Image Interpretation, Computer-Assisted
  • Liver
  • Magnetic Resonance Angiography
  • Meglumine
  • Models, Biological
  • Organometallic Compounds

Identity

PubMed Central ID

  • PMC4058642

Scopus Document Identifier

  • 84902382075

Digital Object Identifier (DOI)

  • 10.1002/jmri.24341

PubMed ID

  • 24923476

Additional Document Info

volume

  • 40

issue

  • 1