Effect of Bias in Contrast Agent Concentration Measurement on Estimated Pharmacokinetic Parameters in Brain Dynamic Contrast-Enhanced Magnetic Resonance Imaging Studies

Document Type : Original Paper


Islamic Azad University Najafabad Branch, Najafabad, Iran


Introduction: Pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely applied in tumor diagnosis and treatment evaluation. Precision analysis of the estimated PK parameters is essential when they are used as a measure for therapy evaluation or treatment planning. In this study, the accuracy of PK parameters in brain DCE-MRI studies was quantified in relation to two major sources of error(including pre-contrast longitudinal-relaxation time, T1,0 and flip angle, α).
Material and Methods: 3470 dynamic contrast-enhanced-curves were simulated using a wide variation of the PK parameters. The bias of contrast concentration due to the systematic biases in α and T1,0 was calculated and added to both contrast concentration and AIF profiles. Thereafter, the PK parameters were estimated for each simulated curve in the presence of different percentages of relative biases in α and T1,0. The mean percentage error (MPE) of PK parameters was then calculated for all simulated curves.
Results: The results indicated that plasma volume(vp) was the most sensitive parameter to bias of contrast concentration, which may overestimate up to 700% in 10% coincidence relative bias in α and T1,0. The lowest MPE was related to the backward transfer constant (kep), which was ~2%-15% in 10% coincidence relative bias in each α and T1, 0.
Conclusion: Utilization of a nested model selection technique, along with an accurate estimator, such as maximum-likelihood estimation, created a unique approach for investigating the effect of the bias in the concentration measurement to the estimated PK parameters without the addition of any extra biases to the parameters during the estimation.


Main Subjects


    1. Aryal MP, Nagaraja TN, Keenan KA, Bagher-Ebadian H, Panda S, Brown SL, Cabral G, Fenstermacher JD, Ewing JR: Dynamic Contrast Enhanced MRI Parameters and Tumor Cellularity in a Rat Model of Cerebral Glioma at 7T. Magn Reson in Med 2014, 71:2206-2214.
    2. Heye AK, Culling RD, Valdés Hernández MdC, Thrippleton MJ, Wardlaw JM: Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. NeuroImage : Clinical 2014, 6:262-274.
    3. Mills SJ, du Plessis D, Pal P, Thompson G, Buonacorrsi G, Soh C, Parker GJ, Jackson A: Mitotic Activity in Glioblastoma Correlates with Estimated Extravascular Extracellular Space Derived from Dynamic Contrast-Enhanced MR Imaging. AJNR Am J Neuroradiol 2015, 37:811-817.
    4. Dehkordi ANV, Kamali-Asl A, Wen N, Mikkelsen T, Chetty IJ, Bagher-Ebadian H: DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique. NMR Biomed 2017, 30.
    5. Bagher-Ebadian H, Jain R, Nejad-Davarani SP, Mikkelsen T, Lu M, Jiang Q, Scarpace L, Arbab AS, Narang J, Soltanian-Zadeh H, et al: Model selection for DCE-T1 studies in glioblastoma. Magn Reson Med 2012, 68:241-251.
    6. Wang P, Xue Y, Zhao X, Yu J, Rosen M, Song HK: Effects of Flip Angle Uncertainty and Noise on the Accuracy of DCE-MRI Metrics: Comparison Between Standard Concentration-Based and Signal Difference Methods. Magnetic resonance imaging 2015, 33:166-173.
    7. Giovanni PD, Azlan CA, Ahearn TS, Semple SI, Gilbert FJ, Redpath TW: The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T. Physics in Medicine & Biology 2010, 55:121.
    8. Subashi E, Choudhury KR, Johnson GA: An analysis of the uncertainty and bias in DCE-MRI measurements using the spoiled gradient-recalled echo pulse sequence. Med Phys 2014, 41:032301.
    9. Calamante F: Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc 2013, 74:1-32.
    10. Keil VC, Mädler B, Gieseke J, Fimmers R, Hattingen E, Schild HH, Hadizadeh DR: Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magnetic Resonance Imaging 2017, 40:83-90.
    11. Schabel MC, Parker DL: Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences. Phys Med Biol 2008, 53:2345-2373.
    12. De Naeyer D, De Deene Y, Ceelen WP, Segers P, Verdonck P: Precision analysis of kinetic modelling estimates in dynamic contrast enhanced MRI. Magnetic Resonance Materials in Physics, Biology and Medicine 2011, 24:51-66.
    13. Kershaw LE, Buckley DL: Precision in measurements of perfusion and microvascular permeability with T1-weighted dynamic contrast-enhanced MRI. Magn Reson Med 2006, 56:986-992.
    14. Ewing JR, Bagher-Ebadian H: Model Selection in Measures of Vascular Parameters using Dynamic Contrast Enhanced MRI: Experimental and Clinical Applications. NMR in biomedicine 2013, 26:1028-1041.
    15. Bagher-Ebadian H, Dehkordi A, Ewing J: SU-F-I-26: Maximum Likelihood and Nested Model Selection Techniques for Pharmacokinetic Analysis of Dynamic Contrast Enhanced MRI in Patients with Glioblastoma Tumors. Medical Physics 2016, 43:3392-3392.
    16. Dehkordi ANV, Alireza KA, R. EJ, Ning W, J. CI, Hassan BE: An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies. NMR in Biomedicine 2017, 30:e3682.
    17. Tofts PS, Berkowitz B, Schnall MD: Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med 1995, 33:564-568.
    18. Rohrer M, Bauer H, Mintorovitch J, Requardt M, Weinmann HJ: Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Invest Radiol 2005, 40:715-724.
    19. Gelman N, Ewing JR, Gorell JM, Spickler EM, Solomon EG: Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: relation to estimated iron and water contents. Magn Reson Med 2001, 45:71-79.
    20. Deichmann R: Fast high-resolution T1 mapping of the human brain. Magn Reson Med 2005, 54:20-27.
    21. Myung IJ: Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 2003, 47:90-100.
    22. Ewing JR, Knight RA, Nagaraja TN, Yee JS, Nagesh V, Whitton PA, Li L, Fenstermacher JD: Patlak plots of Gd-DTPA MRI data yield blood–brain transfer constants concordant with those of 14C-sucrose in areas of blood–brain opening. Magnetic Resonance in Medicine 2003, 50:283-292.
    23. Jia ZZ, Gu HM, Zhou XJ, Shi JL, Li MD, Zhou GF, Wu XH: The assessment of immature microvascular density in brain gliomas with dynamic contrast-enhanced magnetic resonance imaging. Eur J Radiol 2015, 84:1805-1809.
    24. Li X, Zhu Y, Kang H, Zhang Y, Liang H, Wang S, Zhang W: Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging 2015, 15:4.
    25. Treier R, Steingoetter A, Fried M, Schwizer W, Boesiger P: Optimized and combined T1 and B1 mapping technique for fast and accurate T1 quantification in contrast-enhanced abdominal MRI. Magn Reson Med 2007, 57:568-576.
    26. Yarnykh VL: Actual flip-angle imaging in the pulsed steady state: a method for rapid three-dimensional mapping of the transmitted radiofrequency field. Magn Reson Med 2007, 57:192-200.
    27. Deoni SC, Rutt BK, Peters TM: Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn Reson Med 2003, 49:515-526.
Volume 17, Issue 3
May and June 2020
Pages 142-152
  • Receive Date: 26 June 2019
  • Revise Date: 06 August 2019
  • Accept Date: 12 August 2019
  • First Publish Date: 01 May 2020