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Influence of B1-Inhomogeneity on Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI: A Simulation Study

  • Park, Bumwoo (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Choi, Byung Se (Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital) ;
  • Sung, Yu Sub (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Woo, Dong-Cheol (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Shim, Woo Hyun (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Kim, Kyung Won (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Choi, Yoon Seok (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Pae, Sang Joon (Department of Surgery, National Health Insurance Service Ilsan Hospital) ;
  • Suh, Ji-Yeon (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Cho, Hyungjoon (Biomedical Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Jeong Kon (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 투고 : 2016.08.16
  • 심사 : 2016.11.18
  • 발행 : 2017.08.01

초록

Objective: To simulate the $B_1$-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods: $B_1$-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast $R_1$, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters ($K^{trans}$, $v_e$, and $v_p$). Results: $B_1$-inhomogeneity resulted in -23-5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: -16.7-61.8% and -16.7-61.8% for the pre-contrast $R_1$, -1.0-0.3% and -5.2-1.3% for the contrast-enhancement ratio, and -14.2-58.1% and -14.1-57.8% for the Gd-concentration, respectively. These resulted in -43.1-48.4% error for $K^{trans}$, -32.3-48.6% error for the $v_e$, and -43.2-48.6% error for $v_p$. The pre-contrast $R_1$ was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a -10% FA error led to a 23.6% deviation in the pre-contrast $R_1$, -0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a -10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were -23.7% for $K^{trans}$, -23.7% for $v_e$, and -23.7% for $v_p$. Conclusion: Even a small degree of $B_1$-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast $R_1$ calculations to FA deviations is a significant cause of the miscalculation.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF), Korea Health Industry Development Institute (KHIDI)

참고문헌

  1. Li SP, Padhani AR. Tumor response assessments with diffusion and perfusion MRI. J Magn Reson Imaging 2012;35:745-763 https://doi.org/10.1002/jmri.22838
  2. Jahng GH, Li KL, Ostergaard L, Calamante F. Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques. Korean J Radiol 2014;15:554-577 https://doi.org/10.3348/kjr.2014.15.5.554
  3. Paik W, Kim HS, Choi CG, Kim SJ. Pre-operative perfusion skewness and kurtosis are potential predictors of progressionfree survival after partial resection of newly diagnosed glioblastoma. Korean J Radiol 2016;17:117-126 https://doi.org/10.3348/kjr.2016.17.1.117
  4. Wang CH, Yin FF, Horton J, Chang Z. Review of treatment assessment using DCE-MRI in breast cancer radiation therapy. World J Methodol 2014;4:46-58 https://doi.org/10.5662/wjm.v4.i2.46
  5. van Schie JJ, Lavini C, van Vliet LJ, Vos FM. Feasibility of a fast method for B1-inhomogeneity correction for FSPGR sequences. Magn Reson Imaging 2015;33:312-318 https://doi.org/10.1016/j.mri.2014.10.008
  6. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999;10:223-232 https://doi.org/10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S
  7. Azlan CA, Di Giovanni P, Ahearn TS, Semple SI, Gilbert FJ, Redpath TW. B1 transmission-field inhomogeneity and enhancement ratio errors in dynamic contrast-enhanced MRI (DCE-MRI) of the breast at 3T. J Magn Reson Imaging 2010;31:234-239 https://doi.org/10.1002/jmri.22018
  8. Dietrich O, Reiser MF, Schoenberg SO. Artifacts in 3-T MRI: physical background and reduction strategies. Eur J Radiol 2008;65:29-35 https://doi.org/10.1016/j.ejrad.2007.11.005
  9. Di Giovanni P, 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. Phys Med Biol 2010;55:121-132 https://doi.org/10.1088/0031-9155/55/1/008
  10. Kuhl CK, Kooijman H, Gieseke J, Schild HH. Effect of B1 inhomogeneity on breast MR imaging at 3.0 T. Radiology 2007;244:929-930 https://doi.org/10.1148/radiol.2443070266
  11. Sung K, Daniel BL, Hargreaves BA. Transmit B1+ field inhomogeneity and T1 estimation errors in breast DCE-MRI at 3 tesla. J Magn Reson Imaging 2013;38:454-459 https://doi.org/10.1002/jmri.23996
  12. 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 https://doi.org/10.1002/mrm.21120
  13. Ahearn TS, Staff RT, Redpath TW, Semple SI. The use of the Levenberg-Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data. Phys Med Biol 2005;50:N85-N92 https://doi.org/10.1088/0031-9155/50/9/N02
  14. Deoni SC. High-resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with highspeed incorporation of RF field inhomogeneities (DESPOT1-HIFI). J Magn Reson Imaging 2007;26:1106-1111 https://doi.org/10.1002/jmri.21130
  15. Schabel MC, Morrell GR. Uncertainty in T(1) mapping using the variable flip angle method with two flip angles. Phys Med Biol 2009;54:N1-N8 https://doi.org/10.1088/0031-9155/54/1/001
  16. Quantitative Imaging Biomarkers Alliance. Profile: DCE MRI Quantification. Available at: http://qibawiki.rsna.org/images/7/7b/DCEMRIProfile_v1_6-20111213.pdf. Accessed December 23, 2015
  17. Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, et al. Models and methods for analyzing DCEMRI: a review. Med Phys 2014;41:124301 https://doi.org/10.1118/1.4898202
  18. Weissleder R, Cheng HC, Marecos E, Kwong K, Bogdanov A Jr. Non-invasive in vivo mapping of tumour vascular and interstitial volume fractions. Eur J Cancer 1998;34:1448-1454 https://doi.org/10.1016/S0959-8049(98)00195-6
  19. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997;7:91-101 https://doi.org/10.1002/jmri.1880070113
  20. Willinek WA, Gieseke J, Kukuk GM, Nelles M, Konig R, Morakkabati-Spitz N, et al. Dual-source parallel radiofrequency excitation body MR imaging compared with standard MR imaging at 3.0 T: initial clinical experience. Radiology 2010;256:966-975 https://doi.org/10.1148/radiol.10092127
  21. Pineda FD, Medved M, Fan X, Karczmar GS. B1 and T1 mapping of the breast with a reference tissue method. Magn Reson Med 2016;75:1565-1573 https://doi.org/10.1002/mrm.25751
  22. Brix G, Semmler W, Port R, Schad LR, Layer G, Lorenz WJ. Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr 1991;15:621-628 https://doi.org/10.1097/00004728-199107000-00018
  23. Hoffmann U, Brix G, Knopp MV, Hess T, Lorenz WJ. Pharmacokinetic mapping of the breast: a new method for dynamic MR mammography. Magn Reson Med 1995;33:506-514 https://doi.org/10.1002/mrm.1910330408
  24. Scharf J, Kemmling A, Hess T, Mehrabi A, Kauffmann G, Groden C, et al. Assessment of hepatic perfusion in transplanted livers by pharmacokinetic analysis of dynamic magnetic resonance measurements. Invest Radiol 2007;42:224-229 https://doi.org/10.1097/01.rli.0000255892.07208.f2
  25. Ma HT, Griffith JF, Yeung DK, Leung PC. Modified brix model analysis of bone perfusion in subjects of varying bone mineral density. J Magn Reson Imaging 2010;31:1169-1175 https://doi.org/10.1002/jmri.22164
  26. Kiessling F, Lichy M, Grobholz R, Heilmann M, Farhan N, Michel MS, et al. Simple models improve the discrimination of prostate cancers from the peripheral gland by T1-weighted dynamic MRI. Eur Radiol 2004;14:1793-1801
  27. Sung YS, Park B, Choi Y, Lim HS, Woo DC, Kim KW, et al. Dynamic contrast-enhanced MRI for oncology drug development. J Magn Reson Imaging 2016;44:251-264 https://doi.org/10.1002/jmri.25173
  28. Brix G, Griebel J, Kiessling F, Wenz F. Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements. Eur J Nucl Med Mol Imaging 2010;37 Suppl 1:S30-S51 https://doi.org/10.1007/s00259-010-1448-7

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