• Jacob U Fluckiger, Matthias C Schabel, and Edward V R Dibella.
• Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah, USA. j.fluckiger@utah.edu
• Magn Reson Med. 2009 Dec 1; 62 (6): 1477-86.

AbstractA method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" K(trans) values in tumor regions were not significantly different than the estimated values, 0.99 +/- 0.41 and 0.86 +/- 0.40 min(-1), respectively, P = 0.27. "True" k(ep) values also matched closely, 0.70 +/- 0.24 and 0.65 +/- 0.25 min(-1), P = 0.08. When only tissue curves free of significant vascular contribution are used (v(p) < 0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.

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