title: Dynamic contrast-enhanced MR imaging of carotid atherosclerotic plaque: model selection, reproducibility, and validation.
abstract: OBJECTIVE
  To compare four known pharmacokinetic models for their ability to describe dynamic contrast material-enhanced magnetic resonance (MR) imaging of carotid atherosclerotic plaques, to determine reproducibility, and to validate the results with histologic findings.
  
  
  METHODS
  The study was approved by the institutional medical ethics committee. Written informed consent was obtained from all patients. Forty-five patients with 30%-99% carotid stenosis underwent dynamic contrast-enhanced MR imaging. Plaque enhancement was measured at 16 time points at approximately 25-second image intervals by using a gadolinium-based contrast material. Pharmacokinetic parameters (volume transfer constant, K(trans); extracellular extravascular volume fraction, v(e); and blood plasma fraction, v(p)) were determined by fitting a two-compartment model to plaque and blood gadolinium concentration curves. The relative fit errors and parameter uncertainties were determined to find the most suitable model. Sixteen patients underwent imaging twice to determine reproducibility. Carotid endarterectomy specimens from 16 patients who were scheduled for surgery were collected for histologic validation. Parameter uncertainties were compared with the Wilcoxon signed rank test. Reproducibility was assessed by using the coefficient of variation. Correlation with histologic findings was evaluated with the Pearson correlation coefficient.
  
  
  RESULTS
  The mean relative fit uncertainty (±standard error) for K(trans) was 10% ± 1 with the Patlak model, which was significantly lower than that with the Tofts (20% ± 1), extended Tofts (33% ± 3), and extended graphical (29% ± 3) models (P < .001). The relative uncertainty for v(p) was 20% ± 2 with the Patlak model and was significantly higher with the extended Tofts (46% ± 9) and extended graphical (35% ± 5) models (P < .001). The reproducibility (coefficient of variation) for the Patlak model was 16% for K(trans) and 26% for v(p). Significant positive correlations were found between K(trans) and the endothelial microvessel content determined on histologic slices (Pearson ρ = 0.72, P = .005).
  
  
  CONCLUSIONS
  The Patlak model is most suited for describing carotid plaque enhancement. Correlation with histologic findings validated K(trans) as an indicator of plaque microvasculature, and the reproducibility of K(trans) was good.
authors.affiliation: Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
authors.forename: Michaela E
authors.initials: ME
authors.lastname: Gaens
authors.forename: Walter H
authors.initials: WH
authors.lastname: Backes
authors.forename: Stefan
authors.initials: S
authors.lastname: Rozel
authors.forename: Matthijs
authors.initials: M
authors.lastname: Lipperts
authors.forename: Stefan N
authors.initials: SN
authors.lastname: Sanders
authors.forename: Karolien
authors.initials: K
authors.lastname: Jaspers
authors.forename: Jacques P M
authors.initials: JP
authors.lastname: Cleutjens
authors.forename: Judith C
authors.initials: JC
authors.lastname: Sluimer
authors.forename: Sylvia
authors.initials: S
authors.lastname: Heeneman
authors.forename: Mat J A P
authors.initials: MJ
authors.lastname: Daemen
authors.forename: Rob J T J
authors.initials: RJ
authors.lastname: Welten
authors.forename: Jan-Willem H
authors.initials: JW
authors.lastname: Daemen
authors.forename: Joachim E
authors.initials: JE
authors.lastname: Wildberger
authors.forename: Robert M
authors.initials: RM
authors.lastname: Kwee
authors.forename: M Eline
authors.initials: ME
authors.lastname: Kooi
mesh_terms: D000368:Aged; D000465:Algorithms; D002340:Carotid Artery Diseases / Q000378:metabolism* / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D005260:Female; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA