Skip to main content

Physical Principles of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MRI

  • Chapter
  • First Online:
Functional Neuroradiology

Abstract

The use of dynamic contrast-agent-enhanced magnetic resonance imaging (MRI) can provide insight into hemodynamic processes not detectable using conventional contrast-enhanced magnetic resonance (MR) techniques. This additional data may allow refinement of differential diagnoses based on microvascular physiology. The dominant dynamic gadolinium-based contrast agent (GBCA) injection MRI techniques currently utilized in brain imaging are: (1) T1-weighted dynamic contrast-enhanced (DCE) MRI, and (2) T2/T2*-weighted dynamic susceptibility contrast (DSC) MRI. DSC-MRI is much more commonly used for clinical perfusion imaging of the brain, especially for the evaluation of stroke and tumor. On the other hand, DCE-MRI is the dominant method of dynamic contrast-enhanced MRI outside of the brain. In both DCE-MRI and DSC-MRI, dynamic images are acquired before, during, and after the administration of an exogenous GBCA. This chapter will provide an overview of the general physical principles of these techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 2013;26(8):1004–27.

    PubMed  Google Scholar 

  2. Quarles CC, Bell LC, Stokes AM. Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. NeuroImage. 2019;187:32–55.

    PubMed  Google Scholar 

  3. Jahng G-H, Li K-L, Ostergaard L, Calamante F. Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques. Korean J Radiol. 2014;15(5):554–77.

    PubMed  PubMed Central  Google Scholar 

  4. Georgiou L, Buckley DL. T1-weighted DCE MRI. In: Cercignani M, Dowell NG, Tofts P, editors. Quantitative MRI of the brain: principles of physical measurement. 2nd ed. Boca Raton: CRC Press; 2018. p. 251–68.

    Google Scholar 

  5. Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2018;49(7):101–21.

    Google Scholar 

  6. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10(3):223–32.

    CAS  PubMed  Google Scholar 

  7. O’Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker road-map for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–86.

    PubMed  Google Scholar 

  8. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging. 1997;7(1):91–101.

    CAS  PubMed  Google Scholar 

  9. Yoon H, Park S-H, Ye JC. Improved volumetric imaging for DCE-MRI using parallel imaging and dynamic compressed sensing. IEEE Glob Conf. 2014;2014:483–6.

    Google Scholar 

  10. Espagnet MR, Bangiyev L, Haber M, Block K, Babb J, Ruggiero V, et al. High-resolution DCE-MRI of the pituitary gland using radial k-space acquisition with compressed sensing reconstruction. Am J Neuroradiol. 2015;36(8):1444–9.

    Google Scholar 

  11. Guo Y, Lebel RM, Zhu Y, Lingala SG, Shiroishi MS, Law M, et al. High-resolution whole-brain DCE-MRI using constrained reconstruction: prospective clinical evaluation in brain tumor patients. Med Phys. 2016;43(5):2013–23.

    PubMed  PubMed Central  Google Scholar 

  12. Zhao J, Yang Z-Y, Luo B-N, Yang J-Y, Chu J-P. Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors. PLoS One. 2015;10(9):e0138573.

    PubMed  PubMed Central  Google Scholar 

  13. Abe T, Mizobuchi Y, Nakajima K, Otomi Y, Irahara S, Obama Y, et al. Diagnosis of brain tumors using dynamic contrast-enhanced perfusion imaging with a short acquisition time. Springerplus. 2015;4(1):88.

    PubMed  PubMed Central  Google Scholar 

  14. Haacke EM, Filleti CL, Gattu R, Ciulla C, Al-Bashir A, Suryanarayanan K, et al. New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absoluteT1 values. Magn Reson Med. 2007;58(3):463–72.

    PubMed  Google Scholar 

  15. Tietze A, Mouridsen K, Mikkelsen IK. The impact of reliable prebolus T 1 measurements or a fixed T1 value in the assessment of glioma patients with dynamic contrast enhancing MRI. Neuroradiology. 2015;57(6):561–72.

    PubMed  Google Scholar 

  16. Fennessy FM, Fedorov A, Gupta SN, Schmidt EJ, Tempany CM, Mulkern RV. Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses. Magn Reson Imaging. 2012;30(9):1224–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Besa C, Bane O, Jajamovich G, Marchione J, Taouli B. 3D T1 relaxometry pre and post gadoxetic acid injection for the assessment of liver cirrhosis and liver function. Magn Reson Imaging. 2015;33(9):1075–82.

    PubMed  Google Scholar 

  18. Kim KA, Park M-S, Kim I-S, Kiefer B, Chung W-S, Kim M-J, et al. Quantitative evaluation of liver cirrhosis using T1 relaxation time with 3 tesla MRI before and after oxygen inhalation. J Magn Reson Imaging. 2012;36(2):405–10.

    PubMed  Google Scholar 

  19. Stikov N, Boudreau M, Levesque IR, Tardif CL, Barral JK, Pike GB. On the accuracy of T1mapping: Searching for common ground. Magn Reson Med. 2015;73(3):514–22.

    PubMed  Google Scholar 

  20. Barker GJ, Simmons A, Arridge SR, Tofts PS. A simple method for investigating the effects of non-uniformity of radiofrequency transmission and radiofrequency reception in MRI. Br J Radiol. 1998;71(841):59–67.

    CAS  PubMed  Google Scholar 

  21. Parker GJ, Barker GJ, Tofts PS. Accurate multislice gradient echoT1 measurement in the presence of non-ideal RF pulse shape and RF field nonuniformity. Magn Reson Med. 2001;45(5):838–45.

    CAS  PubMed  Google Scholar 

  22. Dowell NG, Tofts PS. Fast, accurate, and precise mapping of the RF field in vivo using the 180° signal null. Magn Reson Med. 2007;58(3):622–30.

    PubMed  Google Scholar 

  23. Larsson HBW, Courivaud F, Rostrup E, Hansen AE. Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhancedT1-weighted MRI at 3 tesla. Magn Reson Med. 2009;62(5):1270–81.

    PubMed  Google Scholar 

  24. Hansen AE, Pedersen H, Rostrup E, Larsson HB. Partial volume effect (PVE) on the arterial input function (AIF) inT1-weighted perfusion imaging and limitations of the multiplicative rescaling approach. Magn Reson Med. 2009;62(4):1055–9.

    PubMed  Google Scholar 

  25. Sourbron S, Ingrisch M, Siefert A, Resier M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med. 2009;62(1):205–17.

    PubMed  Google Scholar 

  26. Buckley DL, Roberts C, Parker GJM, Logue JP, Hutchinson CE. Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging—initial experience. Radiology. 2004;233(3):709–15.

    PubMed  Google Scholar 

  27. Quarles CC, Gore JC, Xu L, Yankeelov TE. Comparison of dual-echo DSC-MRI- and DCE-MRI-derived contrast agent kinetic parameters. Magn Reson Imaging. 2012;30(7):944–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Narang J, Jain R, Arbab AS, Mikkelsen T, Scarpace L, Rosenblum ML, et al. Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion. Neuro-Oncology. 2011;13(9):1037–46.

    PubMed  PubMed Central  Google Scholar 

  29. Parker GJM, Buckley DL. Tracer kinetic modeling for T1-weighted DCE-MRI. In: Jackson A, Buckley DL, Parker GJM, editors. Dynamic contrast-enhanced MRI in oncology. Berlin: Springer; 2005. p. 81–92.

    Google Scholar 

  30. Paldino MJ, Barboriak DP. Fundamentals of quantitative dynamic contrast-enhanced MR imaging. Magn Reson Imaging Clin N Am. 2009;17(2):277–89.

    PubMed  Google Scholar 

  31. Gribbestad IS, Gjesdal KI, Nilsen G, Lundgren S, Hjelstuen MHB, Jackson A. An introduction to dynamic contrast-enhanced MRI in oncology. In: Jackson A, Buckley DL, Parker GJM, editors. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Berlin: Springer; 2005. p. 81–92.

    Google Scholar 

  32. Padhani AR. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging. 2002;16(4):407–22.

    PubMed  Google Scholar 

  33. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med. 1991;17(2):357–67.

    CAS  PubMed  Google Scholar 

  34. Gerstner ER, Sorensen AG, Jain RK, Batchelor TT. Advances in neuroimaging techniques for the evaluation of tumor growth, vascular permeability, and angiogenesis in gliomas. Curr Opin Neurol. 2008;21(6):728–35.

    PubMed  Google Scholar 

  35. Zaharchuk G. Theoretical basis of hemodynamic MR imaging techniques to measure cerebral blood volume, cerebral blood flow, and permeability. AJNR Am J Neuroradiol. 2007;28(10):1850–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Brix G, Bahner ML, Hoffmann U, Horvath A, Schreiber W. Regional blood flow, capillary permeability, and compartmental volumes: measurement with dynamic CT–initial experience. Radiology. 1999;210(1):269–76.

    CAS  PubMed  Google Scholar 

  37. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3(1):1–7.

    CAS  PubMed  Google Scholar 

  38. Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, Griebel J. Microcirculation and microvasculature in breast tumors: pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52(2):420–9.

    PubMed  Google Scholar 

  39. Sourbron SP, Buckley DL. Tracer kinetic modeling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012;57(2):R1–R33.

    CAS  PubMed  Google Scholar 

  40. Naish JH, Kershaw LE, Buckley DL, et al. Modeling of contrast agent kinetics in the lung using T1-weighted dynamic contrast-enhanced MRI. Magn Reson Med. 2009;61(6):1507–14.

    PubMed  Google Scholar 

  41. Brix G, Zwick S, Kiessling F, Griebel J. Pharmacokinetic analysis of tissue microcirculation using nested models: multimodel interference and parameter identifiability. Med Phys. 2009;36(7):2923–33.

    PubMed  PubMed Central  Google Scholar 

  42. Quantitative Imaging Biomarkers Alliance. Rsna.org. 2019. Available from: https://www.rsna.org/en/research/quantitative-imaging-biomarkers-alliance.

  43. Profiles. Profiles - QIBA Wiki. 2019. Available from https://qibawiki.rsna.org/index.php/Profiles.

  44. Bane O, Hectors SJ, Wagner M, Arlinghaus LL, Aryal MP, Cao Y, et al. Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med. 2018;79(5):2564–75.

    PubMed  Google Scholar 

  45. Quantitative Imaging Data Warehouse (QIDW). 2019. Available from: https://www.rsna.org/en/research/quantitative-imaging-biomarkers-alliance/quantitative-imaging-data-warehouse.

  46. Bliesener Y, Zhong X, Guo Y, Boss M, Bosca R, Laue H, et al. Radiofrequency transmit calibration: a multi-center evaluation of vendor-provided radiofrequency transmit mapping methods. Med Phys. 2019;46(6):2629–37.

    PubMed  PubMed Central  Google Scholar 

  47. Kim H, Mousa M, Schexnailder P, Hergenrother R, Bolding M, Ntsikoussalabongui B, et al. Portable perfusion phantom for quantitative DCE-MRI of the abdomen. Med Phys. 2017;44(10):5198–209.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Jackson A, Jayson GC, Li KL, Zhu XP, Checkley DR, Tessier JJL, et al. Reproducibility of quantitative dynamic contrast-enhanced MRI in newly presenting glioma. Br J Radiol. 2003;76(903):153–62.

    CAS  PubMed  Google Scholar 

  49. Barboriak DP, Zhang Z, Desai P, Snyder BS, Safriel Y, Mckinstry RC, et al. Interreader variability of dynamic contrast-enhanced MRI of recurrent glioblastoma: the multicenter ACRIN 6677/RTOG 0625 study. Radiology. 2019;290(2):467–76.

    PubMed  Google Scholar 

  50. Shiroishi MS, Castellazzi G, Boxerman JL, Damore F, Essig M, Nguyen TB, et al. Principles of T2*-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging. J Magn Reson Imaging. 2014;41(2):296–313.

    PubMed  Google Scholar 

  51. Welker K, Boxerman J, Kalnin A, Kaufmann T, Shiroishi M, Wintermark M. ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. Am J Neuroradiol. 2015;36(6):E41–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Jonathan SV, Vakil P, Jeong Y, Ansari S, Hurley M, Bendok B, Carroll TJ. A radial 3D GRE-EPI pulse sequence with kz blip encoding for whole-brain isotropic 3D perfusion using DSC-MRI bolus tracking with sliding window reconstruction (3D RAZIR). In: Proceedings of the 21st annual meeting of ISMRM. Salt Lake City, UT, USA. 2013, p 582.

    Google Scholar 

  53. Paulson ES, Prah DE, Schmainda KM. Spiral perfusion imaging with consecutive echoes (SPICE) for the simultaneous mapping of DSC- and DCE-MRI parameters in brain tumor patients: theory and initial feasibility. Tomography. 2016;2(4):295–307.

    PubMed  PubMed Central  Google Scholar 

  54. Gelderen P, van Grandin C, Petrella JR, Moonen CTW. Rapid three-dimensional MR imaging method for tracking a bolus of contrast agent through the brain. Radiology. 2000;216(2):603–8.

    PubMed  Google Scholar 

  55. Newbould RD, Skare ST, Jochimsen TH, Alley MT, Moseley ME, Albers GW, Bammer R. Perfusion mapping with multiecho multishot parallel imaging EPI. Magn Reson Med. 2007;58(1):70–81.

    PubMed  PubMed Central  Google Scholar 

  56. Boxerman JL, Hamberg LM, Rosen BR, Weisskoff RM. MR contrast due to intravascular magnetic susceptibility perturbations. Magn Reson Med. 1995;34(4):555–66.

    CAS  PubMed  Google Scholar 

  57. Aronen HJ, Gazit IE, Louis DN, Buchbinder BR, Pardo FS, Weisskoff RM, et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology. 1994;191(1):41–51.

    CAS  PubMed  Google Scholar 

  58. Aronen HJ, Perkio J. Dynamic susceptibility contrast MRI of gliomas. Neuroimaging Clin N Am. 2002;12(4):501–23.

    PubMed  Google Scholar 

  59. Østergaard L, Johannsen P, Høst-Poulsen P, Vestergaard-Poulsen P, Asboe H, Gee AD, Hansen SB, Cold GE, Gjedde A, Gyldensted C. Cerebral blood flow measurements by magnetic resonance imaging bolus tracking: comparison with [15O]H2O positron emission tomography in humans. J Cereb Blood Flow Metab. 1998;18(9):935–40.

    PubMed  Google Scholar 

  60. Østergaard L, Smith DF, Vestergaard-Poulsen P, Hansen S, Gee AD, Gjedde A, Gyldensted C. Absolute cerebral blood flow and blood volume measured by magnetic resonance imaging bolus tracking: comparison with positron emission tomography values. J Cereb Blood Flow Metab. 1998;18(4):425–32.

    PubMed  Google Scholar 

  61. Schmiedeskamp H, Andre JB, Straka M, Christen T, Nagpal S, Recht L, Thomas RP, Zaharchuk G, Bammer R. Simultaneous perfusion and permeability measurements using combined spin- and gradient-echo MRI. J Cereb Blood Flow Metab. 2013;33(5):732–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Schmiedeskamp H, Straka M, Newbould RD, Zaharchuk G, Andre JB, Olivot JM, Moseley ME, Albers GW, Bammer R. Combined spin- and gradient-echo perfusion-weighted imaging. Magn Reson Med. 2012;68(1):30–40.

    PubMed  Google Scholar 

  63. Skinner JT, Robison RK, Elder CP, Newton AT, Damon BM, Quarles CC. Evaluation of a multiple spin- and gradient-echo (SAGE) EPI acquisition with SENSE acceleration: applications for perfusion imaging in and outside the brain. Magn Reson Imaging. 2014;32(10):1171–80.

    PubMed  PubMed Central  Google Scholar 

  64. Stokes AM, Skinner JT, Yankeelov TE, Quarles CC. Assessment of a simplified spin and gradient echo (sSAGE) approach for human brain tumor perfusion imaging. Magn Reson Imaging. 2016;34(9):1248–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Donahue KM, Krouwer HG, Rand SD, Pathak AP, Marszalkowski CS, Censky SC, et al. Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med. 2000;43(6):845–53.

    CAS  PubMed  Google Scholar 

  66. Calamante F, Willats L, Gadian DG, Connelly A. Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke. Magn Reson Med. 2006;55(5):1180–5.

    PubMed  Google Scholar 

  67. Jochimsen TH, Newbould RD, Skare ST, Clayton DB, Albers GW, Moseley ME, Bammer R. Identifying systematic errors in quantitative dynamic susceptibility contrast perfusion imaging by high-resolution multi-echo parallel EPI. NMR Biomed. 2007;20(4):429–38.

    PubMed  PubMed Central  Google Scholar 

  68. Chakhoyan A, Leu K, Pope W, Cloughesy T, Ellingson B. Improved spatiotemporal resolution of dynamic susceptibility contrast perfusion MRI in brain tumors using simultaneous multi-slice echo-planar imaging. Am J Neuroradiol. 2017;39(1):43–5.

    PubMed  Google Scholar 

  69. Osch MJV, Vonken E-JP, Wu O, Viergever MA, Grond JVD, Bakker CJ. Model of the human vasculature for studying the influence of contrast injection speed on cerebral perfusion MRI. Magn Reson Med. 2003;50(3):614–22.

    PubMed  Google Scholar 

  70. Semmineh N, Bell L, Stokes A, Hu L, Boxerman J, Quarles C. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI Using a population-based digital reference object. Am J Neuroradiol. 2018;39(11):1981–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Schmainda K, Prah M, Hu L, Quarles C, Semmineh N, Rand S, et al. Moving toward a consensus DSC-MRI protocol: validation of a low–flip angle single-dose option as a reference standard for brain tumors. Am J Neuroradiol. 2019;40(4):626–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Zierler KL. Circulation times and the theory of indicator-dilution methods for determining blood flow and volume. In: Handbook of physiology. Baltimore: Williams & Wilkins; 1962. p. 585–615.

    Google Scholar 

  73. Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med. 1990;14(2):249–65.

    CAS  PubMed  Google Scholar 

  74. Simonsen CZ, Ostergaard L, Vestergaard-Poulsen P, Rohl L, Bjornerud A, Gyldensted C. CBF and CBV measurements by USPIO bolus tracking: reproducibility and comparison with Gd- based values. J Magn Reson Imaging. 1999;9(2):342–7.

    CAS  PubMed  Google Scholar 

  75. Kiselev VG. On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magn Reson Med. 2001;46(6):1113–22.

    CAS  PubMed  Google Scholar 

  76. Calamante F, Connelly A, Van Osch MJP. Nonlinear ΔR2* effects in perfusion quantification using bolus-tracking MRI. Magn Reson Med. 2009;61(2):486–92.

    PubMed  Google Scholar 

  77. Meier P, Zierler KL. On the theory of the indicator-dilution method for measurement of blood flow and volume. J Appl Physiol. 1954;6(12):731–44.

    CAS  PubMed  Google Scholar 

  78. Lassen NA. Tracer kinetic methods in medical physiology. New York: Raven Press; 1979.

    Google Scholar 

  79. Todd-Pokropek A. Estimating blood flow by deconvolution of the injection of radioisotope tracers. In: Rescigno A, Boicelli A, editors. Cerebral blood flow: mathematical models, instrumentation, and imaging techniques. New York: Plenum Press; 1988. p. 107–19.

    Google Scholar 

  80. Rosen BR, Belliveau JW, Chien D. Perfusion imaging by nuclear magnetic resonance. Magn Reson Q. 1989;5(4):263–81.

    CAS  PubMed  Google Scholar 

  81. Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn Reson Med. 1996;36(5):726–36.

    CAS  PubMed  Google Scholar 

  82. Wu O, Østergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG. Tracer arrival timing-insensitive technique for estimating flow in MR perfusion- weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med. 2003;50(1):164–74.

    PubMed  Google Scholar 

  83. Mouridsen K, Hansen MB, Ostergaard L, Jespersen SN. Reliable estimation of capillary transit time distributions using DSC-MRI. J Cereb Blood Flow Metab. 2014;34(9):1511–21.

    PubMed  PubMed Central  Google Scholar 

  84. Troprès I, Grimault S, Vaeth A, Grillon E, Julien C, Payen JF, Lamalle L, Decorps M. Vessel size imaging. Magn Reson Med. 2001;45(3):397–408.

    PubMed  Google Scholar 

  85. Digernes I, Bjørnerud A, Vatnehol SAS, Løvland G, Courivaud F, Vik-Mo E, et al. A theoretical framework for determining cerebral vascular function and heterogeneity from dynamic susceptibility contrast MRI. J Cereb Blood Flow Metab. 2017;37(6):2237–48.

    PubMed  PubMed Central  Google Scholar 

  86. Troprès I, Pannetier N, Grand S, Lemasson B, Moisan A, Péoch M, et al. Imaging the microvessel caliber and density: principles and applications of microvascular MRI. Magn Reson Med. 2014;73(1):325–41.

    PubMed  Google Scholar 

  87. Rempp KA, Brix G, Wenz F, Becker CR, Guckel F, Lorenz WJ. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology. 1994;193(3):637–41.

    CAS  PubMed  Google Scholar 

  88. Calamante F, Morup M, Hansen LK. Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med. 2004;52(4):789–97.

    PubMed  Google Scholar 

  89. Carroll TJ, Rowley HA, Haughton VM. Automatic calculation of the arterial input function for cerebral perfusion imaging with MR imaging. Radiology. 2003;227(2):593–600.

    PubMed  Google Scholar 

  90. Rausch M, Scheffler K, Rudin M, Radu EW. Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements. Magn Reson Imaging. 2000;18(10):1235–43.

    CAS  PubMed  Google Scholar 

  91. Yang C, Karczmar GS, Medved M, Stadler WM. Estimating the arterial input function using two reference tissues in dynamic contrast-enhanced MRI studies: fundamental concepts and simulations. Magn Reson Med. 2004;52(5):1110–7.

    PubMed  Google Scholar 

  92. Gruner R, Bjornara BT, Moen G, Taxt T. Magnetic resonance brain perfusion imaging with voxel-specific arterial input functions. J Magn Reson Imaging. 2006;23(3):273–84.

    PubMed  Google Scholar 

  93. Bjornerud A, Emblem KE. A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J Cereb Blood Flow Metab. 2010;30(5):1066–78.

    PubMed  PubMed Central  Google Scholar 

  94. Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O’Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy F, Li X. The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge. Tomography. 2016;2(1):56–66.

    PubMed  PubMed Central  Google Scholar 

  95. Calamante F. Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013;74:1–32.

    CAS  PubMed  Google Scholar 

  96. Willats L, Christensen S, Ma HK, Donnan GA, Connelly A, Calamante F. Validating a local arterial input function method for improved perfusion quantification in stroke. J Cereb Blood Flow Metab. 2011;31(11):2189–98.

    PubMed  PubMed Central  Google Scholar 

  97. Nejad-Davarani SP, Bagher-Ebadian H, Ewing JR, Noll DC, Mikkelsen T, Chopp M, Jiang Q. An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images. NMR Biomed. 2017;30:6.

    Google Scholar 

  98. Nejad-Davarani SP, Bagher-Ebadian H, Ewing JR, Noll DC, Mikkelsen T, Chopp M, Jiang Q. A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level. NMR Biomed. 2017;30:5.

    Google Scholar 

  99. Jackson A, O’Connor J, Thompson G, Mills S. Magnetic resonance perfusion imaging in neuro-oncology. Cancer Imaging. 2008;8:186–99.

    PubMed  PubMed Central  Google Scholar 

  100. Wetzel SG, Cha S, Johnson G, et al. Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology. 2002;224(3):797–803.

    PubMed  Google Scholar 

  101. Prah M, Stufflebeam S, Paulson E, Kalpathy-Cramer J, Gerstner E, Batchelor T, et al. Repeatability of standardized and normalized relative CBV in patients with newly diagnosed glioblastoma. Am J Neuroradiol. 2015;36(9):1654–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Bedekar D, Jensen T, Schmainda KM. Standardization of relative cerebral blood volume (rCBV) image maps for ease of both inter- and intrapatient comparisons. Magn Reson Med. 2010;64(3):907–13.

    PubMed  PubMed Central  Google Scholar 

  103. Quarles CC, Gochberg DF, Gore JC, Yankeelov TE. A theoretical framework to model DSC-MRI data acquired in the presence of contrast agent extravasation. Phys Med Biol. 2009;54(19):5749–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Semmineh NB, Xu J, Skinner JT, Xie J, Li H, Ayers G, et al. Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE). Magn Reson Med. 2015;74(3):772–84.

    PubMed  Google Scholar 

  105. Paulson ES, Schmainda KM. Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology. 2008;249(2):601–13.

    PubMed  PubMed Central  Google Scholar 

  106. Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol. 2006;27(4):859–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Hu L, Baxter L, Pinnaduwage D, Paine T, Karis J, Feuerstein B, et al. Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. Am J Neuroradiol. 2009;31(1):40–8.

    PubMed  Google Scholar 

  108. Hu L, Baxter L, Smith K, Feuerstein B, Karis J, Eschbacher J, Coons S, Nakaji P, Yeh R, Debbins J, Heiserman J. Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. Am J Neuroradiol. 2008;30(3):552–8.

    PubMed  Google Scholar 

  109. Leu K, Boxerman JL, Cloughesy TF, Lai A, Nghiemphu PL, Liau LM, Pope WB, Ellingson BM. Improved leakage correction for single-echo dynamic susceptibility contrast perfusion MRI estimates of relative cerebral blood volume in high-grade gliomas by accounting for bidirectional contrast agent exchange. Am J Neuroradiol. 2016;37(8):1440–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A population-based digital reference object (DRO) for optimizing dynamic susceptibility contrast (DSC)-MRI methods for clinical trials. Tomography. 2017;3(1):41–9.

    PubMed  PubMed Central  Google Scholar 

  111. Leu K, Boxerman J, Ellingson B. Effects of MRI protocol parameters, preload injection dose, fractionation strategies, and leakage correction algorithms on the fidelity of dynamic-susceptibility contrast MRI estimates of relative cerebral blood volume in gliomas. Am J Neuroradiol. 2016;38(3):478–84.

    PubMed  Google Scholar 

  112. Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first-pass gradient- echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol. 2004;25(9):1524–32.

    PubMed  PubMed Central  Google Scholar 

  113. Stokes AM, Semmineh N, Quarles CC. Validation of a T1 and T2* leakage correction method based on multiecho dynamic susceptibility contrast MRI using MION as a reference standard. Magn Reson Med. 2016;76(2):613–25.

    CAS  PubMed  Google Scholar 

  114. Varallyay CG, Nesbit E, Horvath A, Varallyay P, Fu R, Gahramanov S, et al. Cerebral blood volume mapping with ferumoxytol in dynamic susceptibility contrast perfusion MRI: comparison to standard of care. J Magn Reson Imaging. 2018;48(2):441–8.

    PubMed  PubMed Central  Google Scholar 

  115. Vasanawala SS, Nguyen K-L, Hope MD, Bridges MD, Hope TA, Reeder SB, et al. Safety and technique of ferumoxytol administration for MRI. Magn Reson Med. 2016;75(5):2107–11.

    PubMed  PubMed Central  Google Scholar 

  116. Neuwelt EA, et al. The potential of ferumoxytol nanoparticle magnetic resonance imaging, perfusion, and angiography in central nervous system malignancy: a pilot study. Neurosurgery. 2007;60(4):601–11.

    PubMed  Google Scholar 

  117. Varallyay CG, Nesbit E, Fu R, Gahramanov S, Moloney B, Earl E, Muldoon LL, Li X, Rooney WD, Neuwelt EA. High-resolution steady-state cerebral blood volume maps in patients with central nervous system neoplasms using ferumoxytol, a superparamagnetic iron oxide nanoparticle. J Cereb Blood Flow Metab. 2013;33(5):780–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Gahramanov S, Muldoon LL, Varallyay CG, Li X, Kraemer DF, Fu R, Hamilton BE, Rooney WD, Neuwelt EA. Pseudoprogression of glioblastoma after chemo- and radiation therapy: diagnosis by using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging with ferumoxytol versus gadoteridol and correlation with survival. Radiology. 2013;266(3):842–52.

    PubMed  PubMed Central  Google Scholar 

  119. Gahramanov S, Raslan A, Muldoon L, Hamilton B, Rooney W, Varallyay C, et al. Potential for differentiation of pseudoprogression from true tumor progression with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging using ferumoxytol vs. gadoteridol: a pilot study. Int J Radiat. 2011;79(2):514–23.

    Google Scholar 

  120. Patel P, Baradaran H, Delgado D, Askin G, Christos P, Tsiouris AJ, et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro-Oncology. 2017;19(1):118–27.

    PubMed  Google Scholar 

  121. Boxerman JL, Prah D, Paulson E, Machan J, Bedekar D, Schmainda K. The role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard. Am J Neuroradiol. 2012;33(6):1081–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Ellingson BM, Bendszus M, Boxerman J, Barboriak D, Erickson BJ, Smits M, et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro-Oncology. 2015;17(9):1188–98.

    PubMed  PubMed Central  Google Scholar 

  123. Schmainda K, Prah M, Rand S, Liu Y, Logan B, Muzi M, et al. Multisite concordance of DSC-MRI analysis for brain tumors: results of a national cancer institute quantitative imaging network collaborative project. Am J Neuroradiol. 2018;39(6):1008–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  124. DSC MRI Biomarker Ctte. DSC MRI Biomarker Ctte - QIBA Wiki. 2019. Available from https://qibawiki.rsna.org/index.php/DSC_MRI_Biomarker_Ctte

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mark S. Shiroishi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shiroishi, M.S. et al. (2023). Physical Principles of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MRI. In: Faro, S.H., Mohamed, F.B. (eds) Functional Neuroradiology. Springer, Cham. https://doi.org/10.1007/978-3-031-10909-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10909-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10908-9

  • Online ISBN: 978-3-031-10909-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics