Skip to main content
SpringerLink
Log in

MR Perfusion Imaging: ASL, T2*-Weighted DSC, and T1-Weighted DCE Methods

  • Chapter
  • First Online:
Functional Brain Tumor Imaging

  • 2371 Accesses

Abstract

State-of-the art imaging evaluation of brain tumors has extended beyond conventional contrast-enhanced MR imaging. Functional imaging assessment of brain tumor vascularity is important because there is a close association between angiogenesis and prognosis. MR perfusion imaging, including arterial spin labeling, T2*-weighted dynamic susceptibility contrast (DSC), and T1-weighted dynamic contrast-enhanced methods, can provide the radiologist with quantitative imaging biomarkers for characterization of tumor vascularity that can be helpful to determine tumor grading, predict prognosis, and evaluate therapeutic efficacy. This is particularly crucial as current and future anticancer agents may target specific aspects of tumor biology that may not be reflected by assessment of simple size measurement on conventional MRI. Technical factors including methods of image acquisition, data post-processing, and interpretation are also discussed.

Modern evaluation of brain tumors has extended beyond conventional contrast-enhanced MR imaging. Relying on the enhancing portion of a brain tumor alone can be problematic because it underestimates the actual tumor volume in high-grade gliomas, and low-grade gliomas often do not enhance to a significant degree (Earnest et al., Radiology 166:823–27, 1988). Incorporation of functional imaging techniques to complement the morphologic information provided by conventional MR imaging allows for a more comprehensive understanding of the complex biology of brain tumors. The development and validation of quantitative noninvasive imaging biomarkers are critical to improve diagnostic, predictive, and therapeutic efficacy evaluation of brain tumors. Functional imaging assessment of brain tumor vascularity is important because there is a close association between angiogenesis and prognosis (Gaa et al., 6:518–22, 1996). Among current advanced MR imaging techniques, MR perfusion imaging has the potential to provide the radiologist with quantitative imaging biomarkers of tumor vascularity and angiogenesis. Despite an abundance of studies, particularly those involving T2*-weighted DSC MR imaging, demonstrating characterization of brain tumor perfusion parameters, these techniques have yet to be implemented in routine clinical practice.

A brain tumor’s angiogenic physiology lends itself to perfusion imaging, where measures of cerebral blood volume, flow, and permeability have been shown to characterize the behavior of various tumor types. Image acquisition and processing are discussed, as well as efforts to standardize these techniques. Although the role of MR perfusion in characterizing brain tumors is constantly evolving, this chapter defines its current applications and limitations. Finally, future directions and prospects are explored.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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. Ft E, Kelly PJ, Scheithauer BW, et al. Cerebral astrocytomas: histopathologic correlation of MR and CT contrast enhancement with stereotactic biopsy. Radiology. 1988;166:823–7.

    Google Scholar 

  2. Gaa J, Warach S, Wen P, Thangaraj V, Wielopolski P, Edelman RR. Noninvasive perfusion imaging of human brain tumors with EPISTAR. Eur Radiol. 1996;6:518–22.

    CAS  PubMed  Google Scholar 

  3. Folkman J, Ingber D. Inhibition of angiogenesis. Semin Cancer Biol. 1992;3:89–96.

    CAS  PubMed  Google Scholar 

  4. Knopp MV, Giesel FL, Marcos H, von Tengg-Kobligk H, Choyke P. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Top Magn Reson Imaging. 2001;12:301–8.

    CAS  PubMed  Google Scholar 

  5. Quant EC, Wen PY. Novel medical therapeutics in glioblastomas, including targeted molecular therapies, current and future clinical trials. Neuroimaging Clin N Am. 2010;20:425–48.

    PubMed  Google Scholar 

  6. Chaudhry IH, O'Donovan DG, Brenchley PE, Reid H, Roberts IS. Vascular endothelial growth factor expression correlates with tumour grade and vascularity in gliomas. Histopathology. 2001;39:409–15.

    CAS  PubMed  Google Scholar 

  7. Varlet P, Guillamo JS, Nataf F, Koziak M, Beuvon F, Daumas-Duport C. Vascular endothelial growth factor expression in oligodendrogliomas: a correlative study with Sainte-Anne malignancy grade, growth fraction and patient survival. Neuropathol Appl Neurobiol. 2000;26:379–89.

    CAS  PubMed  Google Scholar 

  8. Norden AD, Drappatz J, Wen PY. Novel anti-angiogenic therapies for malignant gliomas. Lancet Neurol. 2008;7:1152–60.

    CAS  PubMed  Google Scholar 

  9. Dvorak HF, Brown LF, Detmar M, Dvorak AM. Vascular permeability factor/vascular endothelial growth factor, microvascular hyperpermeability, and angiogenesis. Am J Pathol. 1995;146:1029–39.

    CAS  PubMed  Google Scholar 

  10. Nagy JA, Masse EM, Herzberg KT, et al. Pathogenesis of ascites tumor growth: vascular permeability factor, vascular hyperpermeability, and ascites fluid accumulation. Cancer Res. 1995;55:360–8.

    CAS  PubMed  Google Scholar 

  11. Jain RK, di Tomaso E, Duda DG, Loeffler JS, Sorensen AG, Batchelor TT. Angiogenesis in brain tumours. Nat Rev Neurosci. 2007;8:610–22.

    CAS  PubMed  Google Scholar 

  12. Millauer B, Shawver LK, Plate KH, Risaui W, Ullrich A. Glioblastoma growth inhibited in vivo by a dominant-negative Flk-1 mutant. Nature. 1994; 367:576–9.

    CAS  PubMed  Google Scholar 

  13. Plate KH, Risau W. Angiogenesis in malignant gliomas. Glia. 1995;15:339–47.

    CAS  PubMed  Google Scholar 

  14. Amoroso A, Del Porto F, Di Monaco C, Manfredini P, Afeltra A. Vascular endothelial growth factor: a key mediator of neoangiogenesis. A review. Eur Rev Med Pharmacol Sci. 1997;1:17–25.

    CAS  PubMed  Google Scholar 

  15. Pietsch T, Valter MM, Wolf HK, et al. Expression and distribution of vascular endothelial growth factor protein in human brain tumors. Acta Neuropathol. 1997;93:109–17.

    CAS  PubMed  Google Scholar 

  16. Bergers G, Hanahan D. Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer. 2008;8:592–603.

    CAS  PubMed  Google Scholar 

  17. Argyriou AA, Giannopoulou E, Kalofonos HP. Angiogenesis and anti-angiogenic molecularly targeted therapies in malignant gliomas. Oncology. 2009;77:1–11.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  19. Wintermark M, Sesay M, Barbier E, et al. Comparative overview of brain perfusion imaging techniques. Stroke. 2005;36:e83–99.

    PubMed  Google Scholar 

  20. Knutsson L, van Westen D, Petersen ET, et al. Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling. Magn Reson Imaging. 2010;28:1–7.

    CAS  PubMed  Google Scholar 

  21. Rosen BR, Belliveau JW, Buchbinder BR, et al. Contrast agents and cerebral hemodynamics. Magn Reson Med. 1991;19:285–92.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  23. 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;193:637–41.

    CAS  PubMed  Google Scholar 

  24. Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology. 2002;223: 11–29.

    PubMed  Google Scholar 

  25. 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: 1524–32.

    PubMed  Google Scholar 

  26. 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;27:859–67.

    CAS  PubMed  Google Scholar 

  27. Donahue KM, Krouwer HG, Rand SD, 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;43:845–53.

    CAS  PubMed  Google Scholar 

  28. Zama A, Tamura M, Inoue HK. Three-dimensional observations on microvascular growth in rat glioma using a vascular casting method. J Cancer Res Clin Oncol. 1991;117:396–402.

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  30. Kassner A, Annesley DJ, Zhu XP, et al. Abnormalities of the contrast re-circulation phase in cerebral tumors demonstrated using dynamic susceptibility contrast-enhanced imaging: a possible marker of vascular tortuosity. J Magn Reson Imaging. 2000;11:103–13.

    CAS  PubMed  Google Scholar 

  31. Dennie J, Mandeville JB, Boxerman JL, Packard SD, Rosen BR, Weisskoff RM. NMR imaging of changes in vascular morphology due to tumor angiogenesis. Magn Reson Med. 1998;40:793–9.

    CAS  PubMed  Google Scholar 

  32. Batchelor TT, Sorensen AG, di Tomaso E, et al. AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell. 2007;11:83–95.

    CAS  PubMed  Google Scholar 

  33. Weisskoff R, Boxerman J, Sorensen A, Kulke S, Campbell T, Rosen B. Simultaneous blood volume and permeability mapping using a single Gd-based contrast injection. In: Society of Magnetic Resonance. San Francisco, 1994

    Google Scholar 

  34. Johnson G, Wetzel SG, Cha S, Babb J, Tofts PS. Measuring blood volume and vascular transfer constant from dynamic, T(2)*-weighted contrast-enhanced MRI. Magn Reson Med. 2004;51:961–8.

    PubMed  Google Scholar 

  35. Cha S, Yang L, Johnson G, et al. Comparison of microvascular permeability measurements, K(trans), determined with conventional steady-state T1-weighted and first-pass T2*-weighted MR imaging methods in gliomas and meningiomas. AJNR Am J Neuroradiol. 2006;27:409–17.

    CAS  PubMed  Google Scholar 

  36. Lupo JM, Cha S, Chang SM, Nelson SJ. Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol. 2005;26:1446–54.

    PubMed  Google Scholar 

  37. Calamante F, Thomas DL, Pell GS, Wiersma J, Turner R. Measuring cerebral blood flow using magnetic resonance imaging techniques. J Cereb Blood Flow Metab. 1999;19:701–35.

    CAS  PubMed  Google Scholar 

  38. Farrar TC, Becker ED. Pulsed and fourier transform NMR. Introduction to theory and methods. New York: Academic; 1971.

    Google Scholar 

  39. Villringer A, Rosen BR, Belliveau JW, et al. Dynamic imaging with lanthanide chelates in normal brain: contrast due to magnetic susceptibility effects. Magn Reson Med. 1988;6:164–74.

    CAS  PubMed  Google Scholar 

  40. Fisel CR, Ackerman JL, Buxton RB, et al. MR contrast due to microscopically heterogeneous magnetic susceptibility: numerical simulations and applications to cerebral physiology. Magn Reson Med. 1991;17:336–47.

    CAS  PubMed  Google Scholar 

  41. Majumdar S, Zoghbi SS, Gore JC. Regional differences in rat brain displayed by fast MRI with superparamagnetic contrast agents. Magn Reson Imaging. 1988;6:611–5.

    CAS  PubMed  Google Scholar 

  42. Roberts TP, Mikulis D. Neuro MR: principles. J Magn Reson Imaging. 2007;26:823–37.

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  44. 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;249:601–13.

    PubMed  Google Scholar 

  45. Provenzale JM, Schmainda KM. Perfusion imaging for brain tumor characterization and assessment of treatment response. In: Jolesz FA, Newton HB, editors. Handbook of neuro-oncology neuroimaging. New York: Elsevier Ltd.; 2008. p. 265–77.

    Google Scholar 

  46. Fuss M, Wenz F, Scholdei R, et al. Radiation-induced regional cerebral blood volume (rCBV) changes in normal brain and low-grade astrocytomas: quantification and time and dose-dependent occurrence. Int J Radiat Oncol Biol Phys. 2000;48:53–8.

    CAS  PubMed  Google Scholar 

  47. Hobbs SK, Shi G, Homer R, Harsh G, Atlas SW, Bednarski MD. Magnetic resonance image-guided proteomics of human glioblastoma multiforme. J Magn Reson Imaging. 2003;18:530–6.

    PubMed  Google Scholar 

  48. Giese A, Bjerkvig R, Berens ME, Westphal M. Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol. 2003;21: 1624–36.

    CAS  PubMed  Google Scholar 

  49. Uematsu H, Maeda M, Sadato N, et al. Blood volume of gliomas determined by double-echo dynamic perfusion-weighted MR imaging: a preliminary study. AJNR Am J Neuroradiol. 2001;22:1915–9.

    CAS  PubMed  Google Scholar 

  50. Babu R, Huang PP, Epstein F, Budzilovich GN. Late radiation necrosis of the brain: case report. J Neurooncol. 1993;17:37–42.

    CAS  PubMed  Google Scholar 

  51. Hu LS, Baxter LC, Pinnaduwage DS, et al. Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. AJNR Am J Neuroradiol. 2010;31:40–8.

    CAS  PubMed  Google Scholar 

  52. Weinstein JS, Varallyay CG, Dosa E, et al. Superparamagnetic iron oxide nanoparticles: diagnostic magnetic resonance imaging and potential therapeutic applications in neurooncology and central nervous system inflammatory pathologies, a review. J Cereb Blood Flow Metab. 2010;30:15–35.

    CAS  PubMed  Google Scholar 

  53. Gahramanov S, Raslan AM, Muldoon LL, 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 Oncol Biol Phys. 2011;79:514–23.

    PubMed  Google Scholar 

  54. Neuwelt EA, Varallyay CG, Manninger S, et al. The potential of ferumoxytol nanoparticle magnetic resonance imaging, perfusion, and angiography in central nervous system malignancy: a pilot study. Neurosurgery. 2007;60:601–11. discussion 611-602.

    PubMed  Google Scholar 

  55. Bock JC, Kaufmann F, Felix R. Comparison of gadolinium-DTPA and macromolecular gadolinium-DTPA-polylysine for contrast-enhanced pulmonary time-of-flight magnetic resonance angiography. Invest Radiol. 1996;31:652–7.

    CAS  PubMed  Google Scholar 

  56. Boschi F, Marzola P, Sandri M, et al. Tumor microvasculature observed using different contrast agents: a comparison between Gd-DTPA-Albumin and B-22956/1 in an experimental model of mammary carcinoma. Magma. 2008;21:169–76.

    CAS  PubMed  Google Scholar 

  57. Sirlin CB, Vera DR, Corbeil JA, Caballero MB, Buxton RB, Mattrey RF. Gadolinium-DTPA-dextran: a macromolecular MR blood pool contrast agent. Acad Radiol. 2004;11:1361–9.

    PubMed  Google Scholar 

  58. Lebduskova P, Kotek J, Hermann P, et al. A gadolinium(III) complex of a carboxylic-phosphorus acid derivative of diethylenetriamine covalently bound to inulin, a potential macromolecular MRI contrast agent. Bioconjug Chem. 2004;15:881–9.

    CAS  PubMed  Google Scholar 

  59. Tian M, Wen X, Jackson EF, et al. Pharmacokinetics and magnetic resonance imaging of biodegradable macromolecular blood-pool contrast agent PG-Gd in non-human primates: a pilot study. Contrast Media Mol Imaging. 2011;6:289–97.

    CAS  PubMed  Google Scholar 

  60. Young IR, Cox IJ, Coutts GA, Bydder GM. Some consideration concerning susceptibility, longitudinal relaxation time constants and motion artifact in vivo human spectroscopy. NMR Biomed. 1989;2:329–39.

    CAS  PubMed  Google Scholar 

  61. Lacerda S, Law M. Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimaging Clin N Am. 2009;19:527–57.

    PubMed  Google Scholar 

  62. Cercignani M, Symms MR, Schmierer K, et al. Three-dimensional quantitative magnetisation transfer imaging of the human brain. NeuroImage. 2005; 27:436–41.

    PubMed  Google Scholar 

  63. Parker GJ, Padhani AR. T1-W DCE-MRI: T1-weighted dynamic contrast-enhanced MRI. In: Tofts PS, editor. Quantitative MRI of the brain. Chichester, England: John Wiley & Sons; 2003. p. 341–64.

    Google Scholar 

  64. Tofts PS, Brix G, Buckley DL, 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;10:223–32.

    CAS  PubMed  Google Scholar 

  65. Evelhoch JL. Key factors in the acquisition of contrast kinetic data for oncology. J Magn Reson Imaging. 1999;10:254–9.

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  67. Turetschek K, Floyd E, Helbich T, et al. MRI assessment of microvascular characteristics in experimental breast tumors using a new blood pool contrast agent (MS-325) with correlations to histopathology. J Magn Reson Imaging. 2001;14:237–42.

    CAS  PubMed  Google Scholar 

  68. 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;17:357–67.

    CAS  PubMed  Google Scholar 

  69. Parker GJ, Tofts PS. Pharmacokinetic analysis of neoplasms using contrast-enhanced dynamic magnetic resonance imaging. Top Magn Reson Imaging. 1999;10:130–42.

    CAS  PubMed  Google Scholar 

  70. O'Connor JP, Jackson A, Asselin MC, Buckley DL, Parker GJ, Jayson GC. Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives. Lancet Oncol. 2008;9:766–76.

    PubMed  Google Scholar 

  71. O'Connor JP, Jackson A, Parker GJ, Jayson GC. DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer. 2007;96:189–95.

    PubMed  Google Scholar 

  72. St Lawrence KS, Lee TY. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. J Cereb Blood Flow Metab. 1998;18:1365–77.

    CAS  PubMed  Google Scholar 

  73. Aref M, Chaudhari AR, Bailey KL, Aref S, Wiener EC. Comparison of tumor histology to dynamic contrast enhanced magnetic resonance imaging-based physiological estimates. Magn Reson Imaging. 2008;26:1279–93.

    PubMed  Google Scholar 

  74. Grobner T. Gadolinium: a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant. 2006;21:1104–8.

    CAS  PubMed  Google Scholar 

  75. Wolf RL, Wang J, Wang S, et al. Grading of CNS neoplasms using continuous arterial spin labeled perfusion MR imaging at 3 Tesla. J Magn Reson Imaging. 2005;22:475–82.

    PubMed  Google Scholar 

  76. Wang J, Fernandez-Seara MA, Wang S, St Lawrence KS. When perfusion meets diffusion: in vivo measurement of water permeability in human brain. J Cereb Blood Flow Metab. 2007;27:839–49.

    PubMed  Google Scholar 

  77. Petersen ET, Zimine I, Ho YC, Golay X. Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol. 2006;79:688–701.

    CAS  PubMed  Google Scholar 

  78. Golay X, Guenther M. Arterial spin labelling: final steps to make it a clinical reality. Magma. 2012;25: 79–82.

    PubMed  Google Scholar 

  79. Pollock JM, Tan H, Kraft RA, Whitlow CT, Burdette JH, Maldjian JA. Arterial spin-labeled MR perfusion imaging: clinical applications. Magn Reson Imaging Clin N Am. 2009;17:315–38.

    PubMed  Google Scholar 

  80. Wolf RL, Detre JA. Clinical neuroimaging using arterial spin-labeled perfusion magnetic resonance imaging. Neurotherapeutics. 2007;4:346–59.

    PubMed  Google Scholar 

  81. Wang J, Alsop DC, Song HK, et al. Arterial transit time imaging with flow encoding arterial spin tagging (FEAST). Magn Reson Med. 2003;50:599–607.

    PubMed  Google Scholar 

  82. Petersen ET, Lim T, Golay X. Model-free arterial spin labeling quantification approach for perfusion MRI. Magn Reson Med. 2006;55:219–32.

    PubMed  Google Scholar 

  83. Thomas DL, Lythgoe MF, Calamante F, Gadian DG, Ordidge RJ. Simultaneous noninvasive measurement of CBF and CBV using double-echo FAIR (DEFAIR). Magn Reson Med. 2001;45:853–63.

    CAS  PubMed  Google Scholar 

  84. Kim T, Kim SG. Quantification of cerebral arterial blood volume and cerebral blood flow using MRI with modulation of tissue and vessel (MOTIVE) signals. Magn Reson Med. 2005;54:333–42.

    PubMed  Google Scholar 

  85. Kim T, Kim SG. Quantitative MRI of cerebral arterial blood volume. Open Neuroimag J. 2011;5: 136–45.

    PubMed  Google Scholar 

  86. Alsop DC, Detre JA. Reduced transit-time sensitivity in noninvasive magnetic resonance imaging of human cerebral blood flow. J Cereb Blood Flow Metab. 1996;16:1236–49.

    CAS  PubMed  Google Scholar 

  87. Thompson G, Mills SJ, Stivaros SM, Jackson A. Imaging of brain tumors: perfusion/permeability. Neuroimaging Clin N Am. 2010;20:337–53.

    PubMed  Google Scholar 

  88. Petersen ET, Mouridsen K, Golay X. The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test-retest study. NeuroImage. 2010;49:104–13.

    PubMed  Google Scholar 

  89. Gevers S, van Osch MJ, Bokkers RP, et al. Intra- and multicenter reproducibility of pulsed, continuous and pseudo-continuous arterial spin labeling methods for measuring cerebral perfusion. J Cereb Blood Flow Metab. 2011;31:1706–15.

    CAS  PubMed  Google Scholar 

  90. Edelman RR, Siewert B, Adamis M, Gaa J, Laub G, Wielopolski P. Signal targeting with alternating radiofrequency (STAR) sequences: application to MR angiography. Magn Reson Med. 1994;31:233–8.

    CAS  PubMed  Google Scholar 

  91. Kwong KK, Chesler DA, Weisskoff RM, et al. MR perfusion studies with T1-weighted echo planar imaging. Magn Reson Med. 1995;34:878–87.

    CAS  PubMed  Google Scholar 

  92. Kim HS, Kim SY. A prospective study on the added value of pulsed arterial spin-labeling and apparent diffusion coefficients in the grading of gliomas. AJNR Am J Neuroradiol. 2007;28:1693–9.

    CAS  PubMed  Google Scholar 

  93. Wolff SD, Balaban RS. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magn Reson Med. 1989;10:135–44.

    CAS  PubMed  Google Scholar 

  94. Henkelman RM, Huang X, Xiang QS, Stanisz GJ, Swanson SD, Bronskill MJ. Quantitative interpretation of magnetization transfer. Magn Reson Med. 1993;29:759–66.

    CAS  PubMed  Google Scholar 

  95. Golay X, Hendrikse J, Lim TC. Perfusion imaging using arterial spin labeling. Top Magn Reson Imaging. 2004;15:10–27.

    PubMed  Google Scholar 

  96. Wang J, Alsop DC, Li L, et al. Comparison of quantitative perfusion imaging using arterial spin labeling at 1.5 and 4.0 Tesla. Magn Reson Med. 2002;48: 242–54.

    PubMed  Google Scholar 

  97. Yongbi MN, Yang Y, Frank JA, Duyn JH. Multislice perfusion imaging in human brain using the C-FOCI inversion pulse: comparison with hyperbolic secant. Magn Reson Med. 1999;42:1098–105.

    CAS  PubMed  Google Scholar 

  98. Wu WC, Jiang SF, Yang SC, Lien SH. Pseudocontinuous arterial spin labeling perfusion magnetic resonance imaging: a normative study of reproducibility in the human brain. NeuroImage. 2011;56:1244–50.

    PubMed  Google Scholar 

  99. Yongbi MN, Fera F, Yang Y, Frank JA, Duyn JH. Pulsed arterial spin labeling: comparison of multisection baseline and functional MR imaging perfusion signal at 1.5 and 3.0 T: initial results in six subjects. Radiology. 2002;222:569–75.

    PubMed  Google Scholar 

  100. Wang Z, Wang J, Detre JA. Improved data reconstruction method for GRAPPA. Magn Reson Med. 2005;54:738–42.

    PubMed  Google Scholar 

  101. Fernandez-Seara MA, Wang Z, Wang J, et al. Continuous arterial spin labeling perfusion measurements using single shot 3D GRASE at 3 T. Magn Reson Med. 2005;54:1241–7.

    PubMed  Google Scholar 

  102. Fernandez-Seara MA, Wang J, Wang Z, et al. Imaging mesial temporal lobe activation during scene encoding: comparison of fMRI using BOLD and arterial spin labeling. Hum Brain Mapp. 2007;28:1391–400.

    PubMed  Google Scholar 

  103. Warmuth C, Gunther M, Zimmer C. Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology. 2003;228:523–32.

    PubMed  Google Scholar 

  104. Law M, Yang S, Wang H, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol. 2003;24:1989–98.

    PubMed  Google Scholar 

  105. Sugahara T, Korogi Y, Kochi M, et al. Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol. 1998;171:1479–86.

    CAS  PubMed  Google Scholar 

  106. Aronen HJ, Glass J, Pardo FS, et al. Echo-planar MR cerebral blood volume mapping of gliomas. Clinical utility. Acta Radiol. 1995;36:520–8.

    CAS  PubMed  Google Scholar 

  107. Barajas Jr RF, Chang JS, Segal MR, et al. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2009;253:486–96.

    PubMed  Google Scholar 

  108. Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology. 1994;191:41–51.

    CAS  PubMed  Google Scholar 

  109. Aronen HJ, Pardo FS, Kennedy DN, et al. High microvascular blood volume is associated with high glucose uptake and tumor angiogenesis in human gliomas. Clin Cancer Res. 2000;6:2189–200.

    CAS  PubMed  Google Scholar 

  110. Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology. 1999;211:791–8.

    CAS  PubMed  Google Scholar 

  111. Shin JH, Lee HK, Kwun BD, et al. Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol. 2002;179:783–9.

    PubMed  Google Scholar 

  112. Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z. Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? AJNR Am J Neuroradiol. 2009;30:681–8.

    CAS  PubMed  Google Scholar 

  113. Law M, Young RJ, Babb JS, et al. Gliomas: Predicting Time to Progression or Survival with Cerebral Blood Volume Measurements at Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging. Radiology. 2008;247:490–8. doi:10.1148/radiol.2472070898.

    PubMed  Google Scholar 

  114. Danchaivijitr N, Waldman AD, Tozer DJ, et al. Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? Radiology. 2008; 247:170–8.

    PubMed  Google Scholar 

  115. Caseiras GB, Chheang S, Babb J, et al. Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting. Eur J Radiol. 2010;73:215–20.

    PubMed  Google Scholar 

  116. Patankar TF, Haroon HA, Mills SJ, et al. Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol. 2005;26:2455–65.

    PubMed  Google Scholar 

  117. Law M, Yang S, Babb JS, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol. 2004;25:746–55.

    PubMed  Google Scholar 

  118. Zhang N, Zhang L, Qiu B, Meng L, Wang X, Hou BL. Correlation of volume transfer coefficient K(trans) with histopathologic grades of gliomas. J Magn Reson Imaging. 2012;36:355–63.

    CAS  PubMed  Google Scholar 

  119. Nguyen TB, Cron GO, Mercier JF, et al. Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas. AJNR Am J Neuroradiol. 2012;33:1539–45.

    CAS  PubMed  Google Scholar 

  120. Foottit C, Cron GO, Hogan MJ, Nguyen TB, Cameron I. Determination of the venous output function from MR signal phase: feasibility for quantitative DCE-MRI in human brain. Magn Reson Med. 2010;63:772–81.

    PubMed  Google Scholar 

  121. Cron GO, Foottit C, Yankeelov TE, Avruch LI, Schweitzer ME, Cameron I. Arterial input functions determined from MR signal magnitude and phase for quantitative dynamic contrast-enhanced MRI in the human pelvis. Magn Reson Med. 2011;66:498–504.

    PubMed  Google Scholar 

  122. Awasthi R, Rathore RK, Soni P, et al. Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers. Neuroradiology. 2012;54:205–13.

    PubMed  Google Scholar 

  123. Chenevert TL, Stegman LD, Taylor JM, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000;92:2029–36.

    CAS  PubMed  Google Scholar 

  124. Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999;9:53–60.

    CAS  PubMed  Google Scholar 

  125. Kono K, Inoue Y, Nakayama K, et al. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22:1081–8.

    CAS  PubMed  Google Scholar 

  126. Mills SJ, Soh C, Rose CJ, et al. Candidate biomarkers of extravascular extracellular space: a direct comparison of apparent diffusion coefficient and dynamic contrast-enhanced MR imaging-derived measurement of the volume of the extravascular extracellular space in glioblastoma multiforme. AJNR Am J Neuroradiol. 2010;31:549–53.

    CAS  PubMed  Google Scholar 

  127. Weber MA, Zoubaa S, Schlieter M, et al. Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology. 2006;66: 1899–906.

    CAS  PubMed  Google Scholar 

  128. Weber MA, Thilmann C, Lichy MP, et al. Assessment of irradiated brain metastases by means of arterial spin-labeling and dynamic susceptibility-weighted contrast-enhanced perfusion MRI: initial results. Invest Radiol. 2004;39:277–87.

    PubMed  Google Scholar 

  129. Canale S, Rodrigo S, Tourdias T, et al. Grading of adults primitive glial neoplasms using arterial spin-labeled perfusion MR imaging. J Neuroradiol. 2011;38:207–13.

    CAS  PubMed  Google Scholar 

  130. Hirai T, Kitajima M, Nakamura H, et al. Quantitative blood flow measurements in gliomas using arterial spin-labeling at 3T: intermodality agreement and inter- and intraobserver reproducibility study. AJNR Am J Neuroradiol. 2011;32:2073–9.

    CAS  PubMed  Google Scholar 

  131. van Westen D, Petersen ET, Wirestam R, et al. Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours. MAGMA. 2011;24:211–23.

    PubMed  Google Scholar 

  132. Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology. 2002;222:715–21.

    PubMed  Google Scholar 

  133. Young GS, Setayesh K. Spin-echo echo-planar perfusion MR imaging in the differential diagnosis of solitary enhancing brain lesions: distinguishing solitary metastases from primary glioma. AJNR Am J Neuroradiol. 2009;30:575–7.

    CAS  PubMed  Google Scholar 

  134. Liao W, Liu Y, Wang X, et al. Differentiation of primary central nervous system lymphoma and high-grade glioma with dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging. Acta Radiol. 2009;50:217–25.

    PubMed  Google Scholar 

  135. Saloner D, Uzelac A, Hetts S, Martin A, Dillon W. Modern meningioma imaging techniques. J Neurooncol. 2010;99:333–40.

    CAS  PubMed  Google Scholar 

  136. Martin AJ, Cha S, Higashida RT, et al. Assessment of vasculature of meningiomas and the effects of embolization with intra-arterial MR perfusion imaging: a feasibility study. AJNR Am J Neuroradiol. 2007;28:1771–7.

    CAS  PubMed  Google Scholar 

  137. Zhang H, Rodiger LA, Shen T, Miao J, Oudkerk M. Preoperative subtyping of meningiomas by perfusion MR imaging. Neuroradiology. 2008;50: 835–40.

    PubMed  Google Scholar 

  138. Arai M, Kashihara K, Kaizaki Y. Enhancing gliotic cyst wall with microvascular proliferation adjacent to a meningioma. J Clin Neurosci. 2006;13:136–9.

    PubMed  Google Scholar 

  139. Nakano T, Asano K, Miura H, Itoh S, Suzuki S. Meningiomas with brain edema: radiological characteristics on MRI and review of the literature. Clin Imaging. 2002;26:243–9.

    PubMed  Google Scholar 

  140. Yang S, Law M, Zagzag D, et al. Dynamic contrast-enhanced perfusion MR imaging measurements of endothelial permeability: differentiation between atypical and typical meningiomas. AJNR Am J Neuroradiol. 2003;24:1554–9.

    PubMed  Google Scholar 

  141. Kelly PJ, Daumas-Duport C, Scheithauer BW, Kall BA, Kispert DB. Stereotactic histologic correlations of computed tomography- and magnetic resonance imaging-defined abnormalities in patients with glial neoplasms. Mayo Clin Proc. 1987;62:450–9.

    CAS  PubMed  Google Scholar 

  142. Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ. Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg. 1987;66:865–74.

    CAS  PubMed  Google Scholar 

  143. Lev MH, Rosen BR. Clinical applications of intracranial perfusion MR imaging. Neuroimaging Clin N Am. 1999;9:309–31.

    CAS  PubMed  Google Scholar 

  144. Macdonald DR, Cascino TL, Schold Jr SC, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8:1277–80.

    CAS  PubMed  Google Scholar 

  145. Henegar MM, Moran CJ, Silbergeld DL. Early postoperative magnetic resonance imaging following nonneoplastic cortical resection. J Neurosurg. 1996;84:174–9.

    CAS  PubMed  Google Scholar 

  146. Kumar AJ, Leeds NE, Fuller GN, et al. Malignant gliomas: MR imaging spectrum of radiation therapy- and chemotherapy-induced necrosis of the brain after treatment. Radiology. 2000;217:377–84.

    CAS  PubMed  Google Scholar 

  147. Ulmer S, Braga TA, Barker 2nd FG, Lev MH, Gonzalez RG, Henson JW. Clinical and radiographic features of peritumoral infarction following resection of glioblastoma. Neurology. 2006;67:1668–70.

    CAS  PubMed  Google Scholar 

  148. Finn MA, Blumenthal DT, Salzman KL, Jensen RL. Transient postictal MRI changes in patients with brain tumors may mimic disease progression. Surg Neurol. 2007;67:246–50. discussion 250.

    PubMed  Google Scholar 

  149. Valk PE, Dillon WP. Radiation injury of the brain. AJNR Am J Neuroradiol. 1991;12:45–62.

    CAS  PubMed  Google Scholar 

  150. Hu LS, Baxter LC, Smith KA, et al. 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. AJNR Am J Neuroradiol. 2009;30:552–8.

    CAS  PubMed  Google Scholar 

  151. Hopewell JW, Calvo W, Jaenke R, Reinhold HS, Robbins ME, Whitehouse EM. Microvasculature and radiation damage. Recent Results Cancer Res. 1993;130:1–16.

    CAS  PubMed  Google Scholar 

  152. Wesseling P, Ruiter DJ, Burger PC. Angiogenesis in brain tumors; pathobiological and clinical aspects. J Neurooncol. 1997;32:253–65.

    CAS  PubMed  Google Scholar 

  153. Oh BC, Pagnini PG, Wang MY, et al. Stereotactic radiosurgery: adjacent tissue injury and response after high-dose single fraction radiation: Part I–Histology, imaging, and molecular events. Neurosurgery. 2007;60:31–44. discussion 44–35.

    PubMed  Google Scholar 

  154. Hazle JD, Jackson EF, Schomer DF, Leeds NE. Dynamic imaging of intracranial lesions using fast spin-echo imaging: differentiation of brain tumors and treatment effects. J Magn Reson Imaging. 1997;7:1084–93.

    CAS  PubMed  Google Scholar 

  155. Narang J, Jain R, Arbab AS, 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 Oncol. 2011;13:1037–46.

    PubMed  Google Scholar 

  156. Ozsunar Y, Mullins ME, Kwong K, et al. Glioma recurrence versus radiation necrosis? A pilot comparison of arterial spin-labeled, dynamic susceptibility contrast enhanced MRI, and FDG-PET imaging. Acad Radiol. 2010;17:282–90.

    PubMed  Google Scholar 

  157. Stupp R, Hegi ME, Mason WP, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 2009;10:459–66.

    CAS  PubMed  Google Scholar 

  158. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352:987–96.

    CAS  PubMed  Google Scholar 

  159. Clarke JL, Chang S. Pseudoprogression and pseudoresponse: challenges in brain tumor imaging. Curr Neurol Neurosci Rep. 2009;9:241–6.

    PubMed  Google Scholar 

  160. Brandes AA, Franceschi E, Tosoni A, et al. MGMT Promoter Methylation Status Can Predict the Incidence and Outcome of Pseudoprogression After Concomitant Radiochemotherapy in Newly Diagnosed Glioblastoma Patients. J Clin Oncol. 2008;26:2192–7. doi:10.1200/JCO.2007.14.8163.

    PubMed  Google Scholar 

  161. van Nifterik KA, van den Berg J, van der Meide WF, et al. Absence of the MGMT protein as well as methylation of the MGMT promoter predict the sensitivity for temozolomide. Br J Cancer. 2010;103:29–35.

    PubMed  Google Scholar 

  162. Prados MD. Treatment strategies for patients with recurrent brain tumors. Semin Radiat Oncol. 1991;1:62–8.

    Google Scholar 

  163. Hegi ME, Diserens AC, Gorlia T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352:997–1003.

    CAS  PubMed  Google Scholar 

  164. Young RJ, Gupta A, Shah AD, et al. Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma. Neurology. 2011;76:1918–24.

    CAS  PubMed  Google Scholar 

  165. Mangla R, Singh G, Ziegelitz D, et al. Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology. 2010;256:575–84.

    PubMed  Google Scholar 

  166. Kong DS, Kim ST, Kim EH, et al. Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. AJNR Am J Neuroradiol. 2011;32:382–7.

    PubMed  Google Scholar 

  167. Shiroishi MS, Jones JGA, Ozhand A, et al. Dynamic contrast-enhanced and dynamic susceptibility contrast MR imaging evaluation of true early progression versus pseudoprogression in patients with high-grade gliomas. In:Proceedings of the American Society of Neuroradiology. Seattle, WA, 2011

    Google Scholar 

  168. Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963–72.

    PubMed  Google Scholar 

  169. Pope WB, Hessel C. Response assessment in neuro-oncology criteria: implementation challenges in multicenter neuro-oncology trials. AJNR Am J Neuroradiol. 2011;32:794–7.

    CAS  PubMed  Google Scholar 

  170. Price RE, Langford LA, Jackson EF, Stephens LC, Tinkey PT, Ang KK. Radiation-induced morphologic changes in the rhesus monkey (Macaca mulatta) brain. J Med Primatol. 2001;30:81–7.

    CAS  PubMed  Google Scholar 

  171. Ljubimova NV, Levitman MK, Plotnikova ED, Eidus L. Endothelial cell population dynamics in rat brain after local irradiation. Br J Radiol. 1991;64: 934–40.

    CAS  PubMed  Google Scholar 

  172. Pena LA, Fuks Z, Kolesnick RN. Radiation-induced apoptosis of endothelial cells in the murine central nervous system: protection by fibroblast growth factor and sphingomyelinase deficiency. Cancer Res. 2000;60:321–7.

    CAS  PubMed  Google Scholar 

  173. Li YQ, Chen P, Haimovitz-Friedman A, Reilly RM, Wong CS. Endothelial apoptosis initiates acute blood-brain barrier disruption after ionizing radiation. Cancer Res. 2003;63:5950–6.

    CAS  PubMed  Google Scholar 

  174. Wenz F, Rempp K, Hess T, et al. Effect of radiation on blood volume in low-grade astrocytomas and normal brain tissue: quantification with dynamic susceptibility contrast MR imaging. Am J Roentgenol. 1996;166:187–93.

    CAS  Google Scholar 

  175. Lee MC, Cha S, Chang SM, Nelson SJ. Dynamic susceptibility contrast perfusion imaging of radiation effects in normal-appearing brain tissue: changes in the first-pass and recirculation phases. J Magn Reson Imaging. 2005;21:683–93.

    PubMed  Google Scholar 

  176. Cao Y, Tsien CI, Sundgren PC, et al. Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for prediction of radiation-induced neurocognitive dysfunction. Clin Cancer Res. 2009;15:1747–54.

    CAS  PubMed  Google Scholar 

  177. Pope WB, Lai A, Nghiemphu P, Mischel P, Cloughesy TF. MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy. Neurology. 2006;66:1258–60. doi:10.1212/01.wnl.0000208958.29600.87.

    CAS  PubMed  Google Scholar 

  178. Norden AD, Young GS, Setayesh K, et al. Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology. 2008;70:779–87.

    CAS  PubMed  Google Scholar 

  179. Gerstner ER, Chen PJ, Wen PY, Jain RK, Batchelor TT, Sorensen G. Infiltrative patterns of glioblastoma spread detected via diffusion MRI after treatment with cediranib. Neuro Oncol. 2010;12:466–72.

    CAS  PubMed  Google Scholar 

  180. Pope WB, Kim HJ, Huo J, et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology. 2009;252:182–9.

    PubMed  Google Scholar 

  181. Sorensen AG, Batchelor TT, Zhang WT, et al. A "vascular normalization index" as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res. 2009;69:5296–300.

    CAS  PubMed  Google Scholar 

  182. Brandsma D, van den Bent MJ. Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol. 2009;22:633–8.

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  184. Law M, Young R, Babb J, Pollack E, Johnson G. Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol. 2007;28:761–6.

    CAS  PubMed  Google Scholar 

  185. Emblem KE, Scheie D, Due-Tonnessen P, et al. Histogram analysis of MR imaging-derived cerebral blood volume maps: combined glioma grading and identification of low-grade oligodendroglial subtypes. AJNR Am J Neuroradiol. 2008;29:1664–70.

    CAS  PubMed  Google Scholar 

  186. Galban CJ, Chenevert TL, Meyer CR, et al. The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat Med. 2009;15:572–6.

    CAS  PubMed  Google Scholar 

  187. Tsien C, Galban CJ, Chenevert TL, et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol. 2010;28:2293–9.

    CAS  PubMed  Google Scholar 

  188. Arllinghaus LR, Yankeelov TE. Diffusion-weighted MRI. In: Yankeelov TE, Pickens DR, Price RR, editors. Quantitative MRI in cancer. Boca Raton, FL: CRC Press; 2012.

    Google Scholar 

  189. Moffat BA, Chenevert TL, Lawrence TS, et al. Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A. 2005;102:5524–9.

    CAS  PubMed  Google Scholar 

  190. Gerstner ER, Sorensen AG. Diffusion and diffusion tensor imaging in brain cancer. Semin Radiat Oncol. 2011;21:141–6.

    PubMed  Google Scholar 

  191. Sorensen AG. Perfusion MR, imaging: moving forward. Radiology. 2008;249:416–7.

    PubMed  Google Scholar 

  192. Geer CP, Simonds J, Anvery A, et al. Does MR perfusion imaging impact management decisions for patients with brain tumors? A prospective study. AJNR Am J Neuroradiol. 2012;33:556–62.

    CAS  PubMed  Google Scholar 

  193. Smith JJ, Sorensen AG, Thrall JH. Biomarkers in imaging: realizing radiology's future. Radiology. 2003;227:633–8.

    PubMed  Google Scholar 

  194. Clarke LP, Croft BS, Nordstrom R, Zhang H, Kelloff G, Tatum J. Quantitative imaging for evaluation of response to cancer therapy. Transl Oncol. 2009;2:195–7.

    PubMed  Google Scholar 

  195. Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology. 2011;258:906–14.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Keck Medical Center, Keck School of Medicine, University of Southern California, 1520 San Pablo St., Lower Level Imaging L1600, Los Angeles, CA, 90033, USA

    Mark S. Shiroishi

  2. Division of Neuroradiology, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Jesse G. A. Jones

  3. iCAD, Inc., Nashua, NH, USA

    Naira Muradyan Ph.D.

  4. Department of Radiology, MedImagem-Hospital Beneficencia Portuguesa, São Paulo, SP, Brazil

    Saulo Lacerda M.D.

  5. Department of Radiology, City of Hope, Duarte, CA, USA

    Bihong T. Chen M.D.

  6. Department of Radiology and Otolaryngology, Division of Neuroradiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    John L. Go

  7. Department of Radiology, USC Medical Center, Los Angeles, CA, USA

    Meng Law M.D., M.B.B.S., F.R.A.C.R.

Authors
  1. Mark S. Shiroishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesse G. A. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naira Muradyan Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saulo Lacerda M.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bihong T. Chen M.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John L. Go
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Meng Law M.D., M.B.B.S., F.R.A.C.R.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark S. Shiroishi .

Editor information

Editors and Affiliations

  1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine and The Johns Hopkins Hospital, Baltimore, Maryland, USA

    Jay J. Pillai

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Shiroishi, M.S. et al. (2014). MR Perfusion Imaging: ASL, T2*-Weighted DSC, and T1-Weighted DCE Methods. In: Pillai, J. (eds) Functional Brain Tumor Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5858-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-5858-7_1

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-5857-0

  • Online ISBN: 978-1-4419-5858-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation