title: DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Translational and Molecular Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
authors.forename: Shimon
authors.initials: S
authors.lastname: Aronhime
authors.forename: Claudia
authors.initials: C
authors.lastname: Calcagno
authors.forename: Guido H
authors.initials: GH
authors.lastname: Jajamovich
authors.forename: Hadrien Arezki
authors.initials: HA
authors.lastname: Dyvorne
authors.forename: Philip
authors.initials: P
authors.lastname: Robson
authors.forename: Douglas
authors.initials: D
authors.lastname: Dieterich
authors.forename: M Isabel
authors.initials: MI
authors.lastname: Fiel
authors.forename: Valérie
authors.initials: V
authors.lastname: Martel-Laferriere
authors.forename: Manjil
authors.initials: M
authors.lastname: Chatterji
authors.forename: Henry
authors.initials: H
authors.lastname: Rusinek
authors.forename: Bachir
authors.initials: B
authors.lastname: Taouli
mesh_terms: D000368:Aged; D000465:Algorithms; D001783:Blood Flow Velocity; D003287:Contrast Media / Q000493:pharmacokinetics; D005260:Female; D006526:Hepatitis C / Q000473:pathology / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000473:pathology / Q000503:physiopathology*; D008102:Liver Circulation; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008536:Meglumine / Q000031:analogs & derivatives* / Q000493:pharmacokinetics; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015588:Observer Variation; D009942:Organometallic Compounds / Q000493:pharmacokinetics*; D011446:Prospective Studies; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine
keywords: fibrosis; liver; perfusion quantification