Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging

  • Mehran BaboliEmail author
  • Jin Zhang
  • Sungheon Gene Kim
Update on Technological Innovations for Cancer Detection and Treatment (T Dickherber, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Update on Technological Innovations for Cancer Detection and Treatment


Purpose of Review

This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors.

Recent Findings

Rapid development of fast magnetic resonance imaging (MRI) technologies over the last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously.


Diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.


Cancer imaging Diffusion MRI DCE-MRI Perfusion Microstructure Water exchange 


Funding Information

This work was supported in part by grants R01CA160620, R01CA219964, UG3CA228699, and P41EB017183 from the National Institutes of Health.

Compliance with Ethical Standards

Conflict of Interest

Mehran Baboli, Jin Zhang, Sungheon Gene Kim declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


  1. 1.
    Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada.[see comment]. J Natl Cancer Inst. 2000;92(3):205–16.CrossRefGoogle Scholar
  2. 2.
    Chenevert TL, McKeever PE, Ross BD. Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res. 1997;3(9):1457–66.PubMedGoogle Scholar
  3. 3.
    Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000;92(24):2029–36. Scholar
  4. 4.
    Kim S, Loevner L, Quon H, Sherman E, Weinstein G, Kilger A, et al. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res. 2009;15(3):986–94. Scholar
  5. 5.
    Choyke PL, Dwyer AJ, Knopp MV. Functional tumor imaging with dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging. 2003;17(5):509–20. Scholar
  6. 6.
    Provenzale JM, Mukundan S, Barboriak DP. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology. 2006;239(3):632–49. Scholar
  7. 7.
    Tang L, Zhou XJ. Diffusion MRI of cancer: from low to high b-values. J Magn Reson Imaging. 2019;49(1):23–40. Scholar
  8. 8.
    Niendorf T, Dijkhuizen RM, Norris DG, Campagne MV, Nicolay K. Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn Reson Med. 1996;36(6):847–57. Scholar
  9. 9.
    Kiselev VG. Ch 10. The cumulant expansion: an overarching mathematical framework for understanding diffusion NMR. In: Diffusion MRI: theory, methods and applications, by Jones, DK Oxford University Press, New York. 2010.CrossRefGoogle Scholar
  10. 10.
    Jensen JH, Helpern JA, Ramani A, Lu HZ, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53(6):1432–40. Scholar
  11. 11.
    Burcaw LM, Fieremans E, Novikov DS. Mesoscopic structure of neuronal tracts from time-dependent diffusion. NeuroImage. 2015;114:18–37. Scholar
  12. 12.
    Padhani AR, Liu G, Mu-Koh D, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102–25. Scholar
  13. 13.
    Chen L, Liu M, Bao J, Xia Y, Zhang J, Zhang L, et al. The correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-analysis. PLoS One. 2013;8(11):e79008. Scholar
  14. 14.
    Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, Rubino G, et al. Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J Neuro-Oncol. 2000;50(3):215–26. Scholar
  15. 15.
    Matsuki M, Inada Y, Nakai G, Tatsugami F, Tanikake M, Narabayashi I, et al. Diffusion-weighed MR imaging of pancreatic carcinoma. Abdom Imaging. 2007;32(4):481–3. Scholar
  16. 16.
    Muraoka N, Uematsu H, Kimura H, Imamura Y, Fujiwara Y, Murakami M, et al. Apparent diffusion coefficient in pancreatic cancer: characterization and histopathological correlations. J Magn Reson Imaging. 2008;27(6):1302–8. Scholar
  17. 17.
    Wang Y, Chen ZE, Nikolaidis P, McCarthy RJ, Merrick L, Sternick LA, et al. Diffusion-weighted magnetic resonance imaging of pancreatic adenocarcinomas: association with histopathology and tumor grade. J Magn Reson Imaging. 2011;33(1):136–42. Scholar
  18. 18.
    Subhawong TK, Durand DJ, Thawait GK, Jacobs MA, Fayad LM. Characterization of soft tissue masses: can quantitative diffusion weighted imaging reliably distinguish cysts from solid masses? Skelet Radiol. 2013;42(11):1583–92. Scholar
  19. 19.
    Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: clinical applications and emerging techniques. J Magn Reson Imaging. 2017;45(2):337–55. Scholar
  20. 20.
    Galban CJ, Ma B, Malyarenko D, Pickles MD, Heist K, Henry NL, et al. Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One. 2015;10(3):e0122151. Scholar
  21. 21.
    Jansen JF, Stambuk HE, Koutcher JA, Shukla-Dave A. Non-Gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol. 2010;31(4):741–8. Scholar
  22. 22.
    Rosenkrantz AB, Sigmund EE, Johnson G, Babb JS, Mussi TC, Melamed J, et al. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology. 2012;264(1):126–35. Scholar
  23. 23.
    Rosenkrantz AB, Sigmund EE, Winnick A, Niver BE, Spieler B, Morgan GR, et al. Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. Magn Reson Imaging. 2012;30(10):1534–40. Scholar
  24. 24.
    Goshima S, Kanematsu M, Noda Y, Kondo H, Watanabe H, Bae KT. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. AJR Am J Roentgenol. 2015;204(5):W543–9. Scholar
  25. 25.
    Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401–7. Scholar
  26. 26.
    Manenti G, Di Roma M, Mancino S, Bartolucci DA, Palmieri G, Mastrangeli R, et al. Malignant renal neoplasms: correlation between ADC values and cellularity in diffusion weighted magnetic resonance imaging at 3 T. Radiol Med. 2008;113(2):199–213. Scholar
  27. 27.
    Panagiotaki E, Walker-Samuel S, Siow B, Johnson SP, Rajkumar V, Pedley RB, et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 2014;74(7):1902–12. %JCancer Research.CrossRefPubMedGoogle Scholar
  28. 28.
    Reynaud O. Time-dependent diffusion MRI in cancer: tissue modeling and applications. Front Phys. 2017;5(58). doi:
  29. 29.
    Panagiotaki E, Chan RW, Dikaios N, Ahmed HU, O’Callaghan J, Freeman A, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Investig Radiol. 2015;50(4):218–27. Scholar
  30. 30.
    Jiang X, Li H, Xie J, Zhao P, Gore JC, Xu J. Quantification of cell size using temporal diffusion spectroscopy. Magn Reson Med. 2016;75(3):1076–85. Scholar
  31. 31.
    Jiang X, Li H, Xie J, McKinley ET, Zhao P, Gore JC, et al. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy. Magn Reson Med. 2017;78(1):156–64. Scholar
  32. 32.
    Reynaud O, Winters KV, Hoang DM, Wadghiri YZ, Novikov DS, Kim SG. Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas. NMR Biomed. 2016;29(10):1350–63. Scholar
  33. 33.
    Mitra PP, Sen PN, Schwartz LM. Short-time behavior of the diffusion coefficient as a geometrical probe of porous media. Phys Rev B Condens Matter. 1993;47(14):8565–74. Scholar
  34. 34.
    Hope TR, White NS, Kuperman J, Chao Y, Yamin G, Bartch H, et al. Demonstration of non-Gaussian restricted diffusion in tumor cells using diffusion time-dependent diffusion-weighted magnetic resonance imaging contrast. Front Oncol. 2016;6(179):179. Scholar
  35. 35.
    Agre P, Bonhivers M, Borgnia MJ. The aquaporins, blueprints for cellular plumbing systems. J Biol Chem. 1998;273(24):14659–62. Scholar
  36. 36.
    Springer CS Jr, Li X, Tudorica LA, Oh KY, Roy N, Chui SY, et al. Intratumor mapping of intracellular water lifetime: metabolic images of breast cancer? NMR Biomed. 2014;27(7):760–73. Scholar
  37. 37.
    Zhang Y, Poirier-Quinot M, Springer CS Jr, Balschi JA. Active trans-plasma membrane water cycling in yeast is revealed by NMR. Biophys J. 2011;101(11):2833–42. Scholar
  38. 38.
    Nath K, Paudyal R, Nelson DS, Pickup S, Zhou R, Leeper DB et al., editors. Acute changes in cellular-interstitial water exchange rate in DB-1 melanoma xenografts after lonidamine administration as a marker of tumor energetics and ion transport. Proc Intl Soc Magn Reson Med; 2014; Milan, Italy.Google Scholar
  39. 39.
    Pfeuffer J, Flogel U, Dreher W, Leibfritz D. Restricted diffusion and exchange of intracellular water: theoretical modelling and diffusion time dependence of 1H NMR measurements on perfused glial cells. NMR Biomed. 1998;11(1):19–31.<19::AID-NBM499>3.0.CO;2-O.CrossRefPubMedGoogle Scholar
  40. 40.
    Meier C, Dreher W, Leibfritz D. Diffusion in compartmental systems. II. Diffusion-weighted measurements of rat brain tissue in vivo and postmortem at very large b-values. Magn Reson Med. 2003;50(3):510–4. Scholar
  41. 41.
    Aslund I, Nowacka A, Nilsson M, Topgaard D. Filter-exchange PGSE NMR determination of cell membrane permeability. J Magn Reson. 2009;200(2):291–5. Scholar
  42. 42.
    Lasic S, Oredsson S, Partridge SC, Saal LH, Topgaard D, Nilsson M, et al. Apparent exchange rate for breast cancer characterization. NMR Biomed. 2016;29(5):631–9. Scholar
  43. 43.
    Lampinen B, Szczepankiewicz F, van Westen D, Englund E. P CS, Latt J et al. Optimal experimental design for filter exchange imaging: apparent exchange rate measurements in the healthy brain and in intracranial tumors. Magn Reson Med. 2017;77(3):1104–14. Scholar
  44. 44.
    Tian X, Li H, Jiang X, Xie J, Gore JC, Xu J. Evaluation and comparison of diffusion MR methods for measuring apparent transcytolemmal water exchange rate constant. J Magn Reson. 2017;275:29–37. Scholar
  45. 45.
    Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 2010;23(7):698–710. Scholar
  46. 46.
    Zhang J, Lemberskiy G, Fieremans E, Novikov DS, Kim SG, editors. Measuring water exchange rate using time-dependent diffusion MRI. ISMRM MR in Cancer Workshop; 2018; Dublin, Ireland.Google Scholar
  47. 47.
    Essig M, Shiroishi MS, Nguyen TB, Saake M, Provenzale JM, Enterline D, et al. Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol. 2013;200(1):24–34. Scholar
  48. 48.
    Jahng GH, Li KL, Ostergaard L, Calamante F. Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques. Korean J Radiol. 2014;15(5):554–77. Scholar
  49. 49.
    Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed. 2009;22(1):28–39. Scholar
  50. 50.
    Zhang J, Liu H, Tong H, Wang S, Yang Y, Liu G, et al. Clinical applications of contrast-enhanced perfusion MRI techniques in gliomas: recent advances and current challenges. Contrast Media Mol I. 2017;2017:7064120. Scholar
  51. 51.
    Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel’farb G, et al. Models and methods for analyzing DCE-MRI: A review. Med Phys. 2014;41(12):124301. Scholar
  52. 52.
    Paldino MJ, Barboriak DP. Fundamentals of quantitative dynamic contrast-enhanced MR imaging. Magn Reson Imaging Clin N Am. 2009;17(2):277–89. Scholar
  53. 53.
    Tsao J, Kozerke S. MRI temporal acceleration techniques. J Magn Reson Imaging. 2012;36(3):543–60. Scholar
  54. 54.
    Jones RA, Haraldseth O, Muller TB, Rinck PA, Oksendal AN. K-space substitution: a novel dynamic imaging technique. Magn Reson Med. 1993;29(6):830–4.CrossRefGoogle Scholar
  55. 55.
    Kim SG, Freed M, Leite APK, Zhang J, Seuss C, Moy L. Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort. J Magn Reson Imaging. 2017;45(5):1385–93. Scholar
  56. 56.
    Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58(6):1182–95. Scholar
  57. 57.
    Otazo R, Kim D, Axel L, Sodickson DK. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med. 2010;64(3):767–76. Scholar
  58. 58.
    Adluru G, McGann C, Speier P, Kholmovski EG, Shaaban A, Dibella EV. Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging. J Magn Reson Imaging. 2009;29(2):466–73. Scholar
  59. 59.
    Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med. 2014;72(3):707–17. Scholar
  60. 60.
    Winkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O. An optimal radial profile order based on the Golden Ratio for time-resolved MRI. IEEE Trans Med Imaging. 2007;26(1):68–76. Scholar
  61. 61.
    Turkbey B, Thomasson D, Pang Y, Bernardo M, Choyke PL. The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. Diagn Interv Radiol. 2010;16(3):186–92. Scholar
  62. 62.
    Zhang J, Feng L, Otazo R, Kim SG. Rapid dynamic contrast-enhanced MRI for small animals at 7T using 3D ultra-short echo time and golden-angle radial sparse parallel MRI. Magn Reson Med. 2019;81(1):140–52. Scholar
  63. 63.
    Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055–71. Scholar
  64. 64.
    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(3):223–32.<223::AID-JMRI2>3.0.CO;2-S.CrossRefPubMedGoogle Scholar
  65. 65.
    Kim S, Quon H, Loevner LA, Rosen MA, Dougherty L, Kilger AM, et al. Transcytolemmal water exchange in pharmacokinetic analysis of dynamic contrast-enhanced MRI data in squamous cell carcinoma of the head and neck. J Magn Reson Imaging. 2007;26(6):1607–17. Scholar
  66. 66.
    Brookes JA, Redpath TW, Gilbert FJ, Murray AD, Staff RT. Accuracy of T1 measurement in dynamic contrast-enhanced breast MRI using two- and three-dimensional variable flip angle fast low-angle shot. J Magn Reson Imaging. 1999;9(2):163–71.CrossRefGoogle Scholar
  67. 67.
    Scheffler K, Hennig J. T(1) quantification with inversion recovery TrueFISP. Magn Reson Med. 2001;45(4):720–3.CrossRefGoogle Scholar
  68. 68.
    Deoni SC, Rutt BK, Peters TM. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn Reson Med. 2003;49(3):515–26. Scholar
  69. 69.
    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(2):454–9. Scholar
  70. 70.
    Dowell NG, Tofts PS. Fast, accurate, and precise mapping of the RF field in vivo using the 180 degrees signal null. Magn Reson Med. 2007;58(3):622–30. Scholar
  71. 71.
    Wang J, Qiu M, Kim H, Constable RT. T1 measurements incorporating flip angle calibration and correction in vivo. J Magn Reson. 2006;182(2):283–92. Scholar
  72. 72.
    Morrell GR. A phase-sensitive method of flip angle mapping. Magn Reson Med. 2008;60(4):889–94. Scholar
  73. 73.
    Parker GJ, Barker GJ, Tofts PS. Accurate multislice gradient echo T(1) measurement in the presence of non-ideal RF pulse shape and RF field nonuniformity. Magn Reson Med. 2001;45(5):838–45.CrossRefGoogle Scholar
  74. 74.
    Balezeau F, Eliat PA, Cayamo AB, Saint-Jalmes H. Mapping of low flip angles in magnetic resonance. Phys Med Biol. 2011;56(20):6635–47. Scholar
  75. 75.
    Buonincontri G, Sawiak SJ. MR fingerprinting with simultaneous B1 estimation. Magn Reson Med. 2016;76(4):1127–35. Scholar
  76. 76.
    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.<223::AID-JMRI2>3.0.CO;2-S.CrossRefPubMedGoogle Scholar
  77. 77.
    Bergamino M, Bonzano L, Levrero F, Mancardi GL, Roccatagliata L. A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors. Phys Medica. 2014;30(6):635–43. Scholar
  78. 78.
    Zhu XP, Li KL, Kamaly-Asl ID, Checkley DR, Tessier JJ, Waterton JC, et al. Quantification of endothelial permeability, leakage space, and blood volume in brain tumors using combined T1 and T2* contrast-enhanced dynamic MR imaging. J Magn Reson Imaging. 2000;11(6):575–85.CrossRefGoogle Scholar
  79. 79.
    Hunter GJ, Hamberg LM, Choi N, Jain RK, McCloud T, Fischman AJ. Dynamic T1-weighted magnetic resonance imaging and positron emission tomography in patients with lung cancer: correlating vascular physiology with glucose metabolism. Clin Cancer Res. 1998;4(4):949–55.PubMedGoogle Scholar
  80. 80.
    Naish JH, Kershaw LE, Buckley DL, Jackson A, Waterton JC, Parker GJ. Modeling of contrast agent kinetics in the lung using T1-weighted dynamic contrast-enhanced MRI. Magn Reson Med. 2009;61(6):1507–14. Scholar
  81. 81.
    Tofts PS, Berkowitz B, Schnall MD. Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med. 1995;33(4):564–8.CrossRefGoogle Scholar
  82. 82.
    Jackson AS, Reinsberg SA, Sohaib SA, Charles-Edwards EM, Jhavar S, Christmas TJ, et al. Dynamic contrast-enhanced MRI for prostate cancer localization. Br J Radiol. 2009;82(974):148–56. Scholar
  83. 83.
    Yang X, Liang J, Heverhagen JT, Jia G, Schmalbrock P, Sammet S, et al. Improving the pharmacokinetic parameter measurement in dynamic contrast-enhanced MRI by use of the arterial input function: theory and clinical application. Magn Reson Med. 2008;59(6):1448–56. Scholar
  84. 84.
    George ML, Dzik-Jurasz AS, Padhani AR, Brown G, Tait DM, Eccles SA, et al. Non-invasive methods of assessing angiogenesis and their value in predicting response to treatment in colorectal cancer. Br J Surg. 2001;88(12):1628–36. Scholar
  85. 85.
    Kim S, Decarlo L, Cho GY, Jensen JH, Sodickson DK, Moy L, et al. Interstitial fluid pressure correlates with intravoxel incoherent motion imaging metrics in a mouse mammary carcinoma model. NMR Biomed. 2012;25(5):787–94. Scholar
  86. 86.
    Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012;57(2):R1–33. Scholar
  87. 87.
    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(12):1365–77.,812,000-00011.CrossRefPubMedGoogle Scholar
  88. 88.
    Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, et al. 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. Scholar
  89. 89.
    Zhang J, Winters K, Reynaud O, Kim SG. Simultaneous measurement of T1/B1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI. NMR Biomed. 2017;30(9):e3737. Scholar
  90. 90.
    Yarnykh VL. Optimal radiofrequency and gradient spoiling for improved accuracy of T1 and B1 measurements using fast steady-state techniques. Magn Reson Med. 2010;63(6):1610–26. Scholar
  91. 91.
    Landis CS, Li X, Telang FW, Coderre JA, Micca PL, Rooney WD, et al. Determination of the MRI contrast agent concentration time course in vivo following bolus injection: effect of equilibrium transcytolemmal water exchange. Magn Reson Med. 2000;44(4):563–74.CrossRefGoogle Scholar
  92. 92.
    Kim S, Loevner LA, Quon H, Kilger A, Sherman E, Weinstein G, et al. Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging. AJNR Am J Neuroradiol. 2010;31(2):262–8. Scholar
  93. 93.
    Huang W, Li X, Morris EA, Tudorica LA, Seshan VE, Rooney WD, et al. The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo. Proc Natl Acad Sci U S A. 2008;105(46):17943–8. Scholar
  94. 94.
    Li X, Huang W, Morris EA, Tudorica LA, Seshan VE, Rooney WD, et al. Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI. Proc Natl Acad Sci U S A. 2008;105(46):17937–42. Scholar
  95. 95.
    Zhang J, Kim S. Uncertainty in MR tracer kinetic parameters and water exchange rates estimated from T1-weighted dynamic contrast enhanced MRI. Magn Reson Med. 2014;72(2):534–45. Scholar
  96. 96.
    Buckley DL. Shutter-speed dynamic contrast-enhanced MRI: Is it fit for purpose? Magn Reson Med. 2019;81(2):976–88. Scholar
  97. 97.
    Spencer RG, Fishbein KW. Measurement of spin-lattice relaxation times and concentrations in systems with chemical exchange using the one-pulse sequence: breakdown of the Ernst model for partial saturation in nuclear magnetic resonance spectroscopy. J Magn Reson. 2000;142(1):120–35. S1090-7807(99)91925–0.CrossRefPubMedGoogle Scholar
  98. 98.
    Zhang J, Freed M, Rodriguez J, Turnbull D, Kim S, editors. Improved accuracy and precision in estimation of intracellular water lifetime. 21st Annual Meeting of ISMRM; 2013; Salt Lake City, Utah, USA.Google Scholar
  99. 99.
    Nilsson M, Englund E, Szczepankiewicz F, van Westen D, Sundgren PC. Imaging brain tumour microstructure. NeuroImage. 2018;182:232–50. Scholar

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Authors and Affiliations

  1. 1.Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUSA

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