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TOMOGRAPHY, March 2016, Volume 2, Issue 1: 56-66
DOI: 10.18383/j.tom.2015.00184

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Wei Huang1, Yiyi Chen1, Andriy Fedorov2, Xia Li3, Guido H. Jajamovich4, Dariya I. Malyarenko5, Madhava P. Aryal5, Peter S. LaViolette6, Matthew J. Oborski7, Finbarr O’Sullivan8, Richard G. Abramson9, Kourosh Jafari-Khouzani10, Aneela Afzal1, Alina Tudorica1, Brendan Moloney1, Sandeep N. Gupta3, Cecilia Besa4, Jayashree Kalpathy-Cramer10, James M. Mountz7, Charles M. Laymon7, Mark Muzi11, Paul E. Kinahan11, Kathleen Schmainda6, Yue Cao5, Thomas L. Chenevert5, Bachir Taouli4, Thomas E. Yankeelov9, Fiona Fennessy2, and Xin Li1

1Oregon Health and Science University, Portland, Oregon; 2Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts; 3General Electric Global Research, Niskayuna, New York; 4Icahn School of Medicine at Mt Sinai, New York, New York; 5University of Michigan, Ann Arbor, Michigan; 6Medical College of Wisconsin, Milwaukee, Wisconsin; 7University of Pittsburgh, Pittsburgh, Pennsylvania; 8University College, Cork, Ireland; 9Vanderbilt University, Nashville, Tennessee; 10Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; and 11University of Washington, Seattle, Washington.


Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCEMRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.


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