title: Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats.
abstract: When using tracer kinetic modeling to analyze dynamic contrast-enhanced MRI (DCE-MRI) it is necessary to identify an appropriate arterial input function (AIF). The measured AIF is often poorly sampled in both clinical and preclinical MR systems due to the initial rapid increase in contrast agent concentration and the subsequent large-scale signal change that occurs in the arteries. However, little work has been carried out to quantify the sensitivity of tracer kinetic modeling parameters to the form of AIF. Using a preclinical experimental data set, we sought to measure the effect of varying model forms of AIF on the extended Kety compartmental model parameters (K(trans), v(e), and v(p)) through comparison with the results of experimentally acquired high temporal resolution AIFs. The AIF models examined have the potential to be parameterized on lower temporal resolution data to predict the form of the true, higher temporal resolution AIF. The models were also evaluated through application to the population average AIF. It was concluded that, in the instance of low temporal resolution or noisy data, it may be preferable to use a bi-exponential model applied to the raw data AIF, or when individual measurements are not available a bi-exponential model of the average AIF.
authors.affiliation: Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, University of Manchester, Manchester, United Kingdom. deirdre.mcgrath@rmp.uhn.on.ca
authors.forename: Deirdre M
authors.initials: DM
authors.lastname: McGrath
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Bradley
authors.forename: Jean L
authors.initials: JL
authors.lastname: Tessier
authors.forename: Tony
authors.initials: T
authors.lastname: Lacey
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Taylor
authors.forename: Geoffrey J M
authors.initials: GJ
authors.lastname: Parker
mesh_terms: D000465:Algorithms; D000818:Animals; D001158:Arteries / Q000378:metabolism*; D045744:Cell Line, Tumor; D015179:Colorectal Neoplasms / Q000378:metabolism* / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D051381:Rats; D011923:Rats, Nude; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA