title: Toward local arterial input functions in dynamic contrast-enhanced MRI.
abstract: OBJECTIVE
  To present a method for estimating the local arterial input function (AIF) within a dynamic contrast-enhanced MRI scan, based on the alternating minimization with model (AMM) method.
  
  
  METHODS
  This method clusters a subset of data into representative curves, which are then input to the AMM algorithm to return a parameterized AIF and pharmacokinetic parameters. Computer simulations are used to investigate the accuracy with which the AMM is able to estimate the true AIF as a function of the input tissue curves.
  
  
  RESULTS
  Simulations show that a power law relates uncertainty in kinetic parameters and SNR and heterogeneity of the input. Kinetic parameters calculated with the measured AIF are significantly different from those calculated with either a global (P < 0.005) or a local input function (P = 0.0). The use of local AIFs instead of measured AIFs yield mean lesion-averaged parameter changes: K(trans): +24% [+15%, +70%], k(ep): +13% [-36%, +300%]. Globally estimated input functions yield mean lesion-averaged changes: K(trans): +9% [-38%, +65%], k(ep): +13% [-100%, +400%]. The observed improvement in fit quality with local AIFs was found to be significant when additional free parameters were accounted for using the Akaike information criterion.
  
  
  CONCLUSIONS
  Local AIFs result in significantly different kinetic parameter values. The statistically significant improvement in fit quality suggests that changes in parameter estimates using local AIFs reflect differences in underlying tissue physiology.
authors.affiliation: Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Jacob U
authors.initials: JU
authors.lastname: Fluckiger
authors.forename: Matthias C
authors.initials: MC
authors.lastname: Schabel
authors.forename: Edward V R
authors.initials: EV
authors.lastname: DiBella
mesh_terms: D000465:Algorithms; D001158:Arteries / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D055420:Perfusion Imaging / Q000379:methods; D015203:Reproducibility of Results; D012509:Sarcoma / Q000175:diagnosis / Q000473:pathology
chemical_list: D003287:Contrast Media