title: Tracer kinetic modeling in myocardial perfusion quantification using MRI.
abstract: OBJECTIVE
  To investigate and compare several quantification methods of myocardial perfusion measurements, paying special attention to the relation between the techniques and the required measurement duration.
  
  
  METHODS
  Seven patients underwent contrast-enhanced rest and stress cardiac perfusion measurements at 3T. Three slices were acquired in each patient and were divided into 16 segments, leading to 112 rest and stress data curves, which were analyzed using various tracer kinetic models as well as a model-free deconvolution. Plasma flow, plasma volume, and myocardial perfusion reserve were analyzed for the complete acquisition as well as for the first pass data only.
  
  
  RESULTS
  Deconvolution analysis yielded stable results for both rest and stress analysis, while Fermi and one compartment models agree well for first pass data (rest measurements only) and prolonged data acquisition (stress measurements only). More complex models do not yield satisfactory results for the short measurement times investigated in this study.
  
  
  CONCLUSIONS
  When performing MRI-based quantification of myocardial perfusion, care must be taken that the method used is appropriate for the time frame under investigation. When a numerical deconvolution is used instead of tracer kinetic models, more stable results are obtained.
authors.affiliation: Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany; Munich Heart Alliance, Munich, Germany.
authors.forename: Felix
authors.initials: F
authors.lastname: Schwab
authors.forename: Michael
authors.initials: M
authors.lastname: Ingrisch
authors.forename: Roy
authors.initials: R
authors.lastname: Marcus
authors.forename: Fabian
authors.initials: F
authors.lastname: Bamberg
authors.forename: Kristof
authors.initials: K
authors.lastname: Hildebrandt
authors.forename: Christine
authors.initials: C
authors.lastname: Adrion
authors.forename: Christopher
authors.initials: C
authors.lastname: Gliemi
authors.forename: Konstantin
authors.initials: K
authors.lastname: Nikolaou
authors.forename: Maximilian
authors.initials: M
authors.lastname: Reiser
authors.forename: Daniel
authors.initials: D
authors.lastname: Theisen
mesh_terms: D000368:Aged; D000465:Algorithms; D001783:Blood Flow Velocity; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000378:metabolism*; D003326:Coronary Circulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007700:Kinetics; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008657:Metabolic Clearance Rate; D008955:Models, Cardiovascular; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D009942:Organometallic Compounds / Q000493:pharmacokinetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C090600:gadobutrol
keywords: cardiac MRI; model selection; myocardial blood flow; myocardial perfusion reserve; perfusion quantification; tracer kinetics