Pre-clinical Cerebral Perfusion MRI Techniques: Comparison
Please note this is a comparison between Version 2 by Karina Chen and Version 1 by Bram Callewaert.

Alterations to the cerebral microcirculation have been recognized to play a crucial role in the development of neurodegenerative disorders. However, the exact role of the microvascular alterations in the pathophysiological mechanisms often remains poorly understood. The early detection of changes in microcirculation and cerebral blood flow (CBF) can be used to get a better understanding of underlying disease mechanisms. This could be an important step towards the development of new treatment approaches. Animal models allow for the study of the disease mechanism at several stages of development, before the onset of clinical symptoms, and the verification with invasive imaging techniques. Specifically, pre-clinical magnetic resonance imaging (MRI) is an important tool for the development and validation of MRI sequences under clinically relevant conditions. This article reviews MRI strategies providing indirect non-invasive measurements of microvascular changes in the rodent brain that can be used for early detection and characterization of neurodegenerative disorders. The perfusion MRI techniques: Dynamic Contrast Enhanced (DCE), Dynamic Susceptibility Contrast Enhanced (DSC) and Arterial Spin Labeling (ASL), will be discussed, followed by less established imaging strategies used to analyze the cerebral microcirculation: Intravoxel Incoherent Motion (IVIM), Vascular Space Occupancy (VASO), Steady-State Susceptibility Contrast (SSC), Vessel size imaging, SAGE-based DSC, Phase Contrast Flow (PC) Quantitative Susceptibility Mapping (QSM) and quantitative Blood-Oxygenation-Level-Dependent (qBOLD). We will emphasize the advantages and limitations of each strategy, in particular on applications for high-field MRI in the rodent’s brain. 

  • microvasculature
  • brain
  • MRI
  • rodent
  • neurodegenerative disorders
  • perfusion
Please wait, diff process is still running!

References

  1. Tofts, P.S.; Kermode, A.G. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn. Reson. Med. 1991, 17, 357–367.
  2. Essig, M.; Shiroishi, M.S.; Nguyen, T.B.; Saake, M.; Provenzale, J.M.; Enterline, D.; Anzalone, N.; Dörfler, A.; Rovira, À.; Wintermark, M.; et al. Perfusion MRI: The Five Most Frequently Asked Technical Questions. Am. J. Roentgenol. 2013, 200, 24–34.
  3. Gordon, Y.; Partovi, S.; Müller-Eschner, M.; Amarteifio, E.; Bäuerle, T.; Weber, M.-A.; Kauczor, H.-U.; Rengier, F. Dynamic contrast-enhanced magnetic resonance imaging: Fundamentals and application to the evaluation of the peripheral perfusion. Cardiovasc. Diagn. Ther. 2014, 4, 147–164.
  4. Nielsen, T.; Wittenborn, T.; Horsman, M.R. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Preclinical Studies of Antivascular Treatments. Pharmaceutics 2012, 4, 563–589.
  5. Khalifa, F.; Soliman, A.; El-Baz, A.; El-Ghar, M.A.; El-Diasty, T.; Gimel’Farb, G.; Ouseph, R.; Dwyer, A.C. Models and methods for analyzing DCE-MRI: A review. Med Phys. 2014, 41, 124301.
  6. Walker-Samuel, S.; Leach, M.O.; Collins, D.J. Evaluation of response to treatment using DCE-MRI: The relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis. Phys. Med. Biol. 2006, 51, 3593–3602.
  7. Calamante, F. Arterial input function in perfusion MRI: A comprehensive review. Prog. Nucl. Magn. Reson. Spectrosc. 2013, 74, 1–32.
  8. Tofts, P.S.; Brix, G.; Buckley, D.L.; Evelhoch, J.L.; Henderson, E.; Knopp, M.V.; Larsson, H.B.W.; Lee, T.-Y.; Mayr, N.A.; Parker, G.J.M.; 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, 223–232.
  9. Duan, C.; Kallehauge, J.F.; Bretthorst, G.L.; Tanderup, K.; Ackerman, J.J.H.; Garbow, J.R. Are complex DCE-MRI models supported by clinical data? Magn. Reson. Med. 2017, 77, 1329–1339.
  10. Barnes, S.L.; Whisenant, J.G.; Loveless, M.E.; Yankeelov, T.E. Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation. Pharmaceutics 2012, 4, 442–478.
  11. Fruytier, A.-C.; Magat, J.; Colliez, F.; Jordan, B.; Cron, G.; Gallez, B. Dynamic contrast-enhanced MRI in mice at high field: Estimation of the arterial input function can be achieved by phase imaging. Magn. Reson. Med. 2014, 71, 544–550.
  12. Tsao, J.; Kozerke, S. MRI temporal acceleration techniques. J. Magn. Reson. Imaging 2012, 36, 543–560.
  13. Pain, F.; Lanièce, P.; Mastrippolito, R.; Gervais, P.; Hantraye, P.; Besret, L. Arterial Input Function Measurement Without Blood Sampling Using a β-Microprobe in Rats. J. Nucl. Med. 2004, 45, 1577–1582.
  14. Zhou, R.; Pickup, S.; Yankeelov, T.E.; Springer, C.S., Jr.; Glickson, J.D. Simultaneous measurement of arterial input function and tumor pharmacokinetics in mice by dynamic contrast enhanced imaging: Effects of transcytolemmal water exchange. Magn. Reson. Med. 2004, 52, 248–257.
  15. Yankeelov, T.E.; Luci, J.J.; Lepage, M.; Li, R.; Debusk, L.; Lin, P.C.; Price, R.R.; Gore, J.C. Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: A reference region model. Magn. Reson. Imaging 2005, 23, 519–529.
  16. Van Osch, M.J.P.; Vonken, E.-J.P.A.; Viergever, M.A.; van der Grond, J.; Bakker, C.J.G. Measuring the arterial input function with gradient echo sequences. Magn. Reson. Med. 2003, 49, 1067–1076.
  17. McGrath, D.M.; Bradley, D.P.; Tessier, J.L.; Lacey, T.; Taylor, C.J.; Parker, G.J.M. Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magn. Reson. Med. 2009, 61, 1173–1184.
  18. Yankeelov, T.E.; de Busk, L.M.; Billheimer, D.D.; Luci, J.J.; Lin, P.C.; Price, R.R.; Gore, J.C. Repeatability of a reference region model for analysis of murine DCE-MRI data at 7T. J. Magn. Reson. Imaging 2006, 24, 1140–1147.
  19. Ortuño, J.E.; Ledesma-Carbayo, M.J.; Simões, R.V.; Candiota, A.P.; Arús, C.; Santos, A. : A dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data. BMC Bioinform. 2013, 14, 316.
  20. Sedlacik, J.; Myers, A.; Loeffler, R.B.; Williams, R.F.; Davidoff, A.M.; Hillenbrand, C.M. A dedicated automated injection system for dynamic contrast-enhanced MRI experiments in mice. J. Magn. Reson. Imaging 2013, 37, 746–751.
  21. Calamante, F.; Gadian, D.G.; Connelly, A. Quantification of Perfusion Using Bolus Tracking Magnetic Resonance Imaging in Stroke. Stroke 2002, 33, 1146–1151.
  22. Stadler, K.L.; Pease, A.P.; Ballegeer, E.A. Dynamic Susceptibility Contrast Magnetic Resonance Imaging Protocol of the Normal Canine Brain. Front. Veter. Sci. 2017, 4, 41.
  23. Østergaard, L. Principles of cerebral perfusion imaging by bolus tracking. J. Magn. Reson. Imaging 2005, 22, 710–717.
  24. Jin, S.; Kang, M.; Cho, H. Cerebral blood perfusion deficits using dynamic susceptibility contrast MRI with gadolinium chelates in rats with post-ischemic reperfusion without significant dynamic contrast-enhanced MRI-derived vessel permeabilities: A cautionary note. PLoS ONE 2018, 13, e0201076.
  25. Boxerman, J.L.; Prah, D.E.; Paulson, E.S.; Machan, J.T.; Bedekar, D.; Schmainda, K.M. 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, 1081–1087.
  26. Skinner, J.T.; Moots, P.L.; Ayers, G.D.; Quarles, C.C. On the Use of DSC-MRI for Measuring Vascular Permeability. Am. J. Neuroradiol. 2016, 37, 80–87.
  27. Leu, K.; Boxerman, J.L.; Cloughesy, T.F.; Lai, A.; Nghiemphu, P.L.; Liau, L.M.; Pope, W.B.; Ellingson, B.M. 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, 1440–1446.
  28. Uematsu, H.; Maeda, M.; Sadato, N.; Matsuda, T.; Ishimori, Y.; Koshimoto, Y.; Kimura, H.; Yamada, H.; Kawamura, Y.; Yonekura, Y.; et al. Blood volume of gliomas determined by double-echo dynamic perfusion-weighted MR imaging: A preliminary study. Am. J. Neuroradiol. 2001, 22, 1915–1919.
  29. Newton, A.T.; Pruthi, S.; Stokes, A.M.; Skinner, J.T.; Quarles, C.C. Improving Perfusion Measurement in DSC-MR Imaging with Multiecho Information for Arterial Input Function Determination. Am. J. Neuroradiol. 2016, 37, 1237–1243.
  30. Quarles, C.C.; Gore, J.C.; Xu, L.; Yankeelov, T.E. Comparison of dual-echo DSC-MRI- and DCE-MRI-derived contrast agent kinetic parameters. J. Magn. Reson. Imaging 2012, 30, 944–953.
  31. Dennie, J.; Mandeville, J.B.; Boxerman, J.L.; Packard, S.D.; Rosen, B.R.; Weisskoff, R.M. NMR imaging of changes in vascular morphology due to tumor angiogenesis. Magn. Reson. Med. 1998, 40, 793–799.
  32. Stokes, A.M.; Skinner, J.T.; Quarles, C.C. Assessment of a combined spin- and gradient-echo (SAGE) DSC-MRI method for preclinical neuroimaging. J. Magn. Reson. Imaging 2014, 32, 1181–1190.
  33. Calamante, F.; Thomas, D.L.; Pell, G.S.; Wiersma, J.; Turner, R. Measuring Cerebral Blood Flow Using Magnetic Resonance Imaging Techniques. J. Cereb. Blood Flow Metab. 1999, 19, 701–735.
  34. Schmiedeskamp, H.; Straka, M.; Newbould, R.D.; Zaharchuk, G.; Andre, J.B.; Olivot, J.-M.; Moseley, M.E.; Albers, G.W.; Bammer, R. Combined spin- and gradient-echo perfusion-weighted imaging. Magn. Reson. Med. 2012, 68, 30–40.
  35. Ferré, J.-C.; Bannier, E.; Raoult, H.; Mineur, G.; Carsin-Nicol, B.; Gauvrit, J.-Y. Arterial spin labeling (ASL) perfusion: Techniques and clinical use. Diagn. Interv. Imaging 2013, 94, 1211–1223.
  36. Alsaedi, A.; Thomas, D.; Bisdas, S.; Golay, X. Overview and Critical Appraisal of Arterial Spin Labelling Technique in Brain Perfusion Imaging. Contrast Media Mol. Imaging 2018, 2018, 1–15.
  37. Petersen, E.T.; Lim, T.; Golay, X. Model-free arterial spin labeling quantification approach for perfusion MRI. Magn. Reson. Med. 2006, 55, 219–232.
  38. Brookes, M.J.; Morris, P.G.; Gowland, P.A.; Francis, S.T. Noninvasive measurement of arterial cerebral blood volume using look-locker EPI and arterial spin labeling. Magn. Reson. Med. 2007, 58, 41–54.
  39. Wang, J.; Alsop, D.C.; Song, H.K.; Maldjian, J.A.; Tang, K.; Salvucci, A.E.; Detre, J.A. Arterial transit time imaging with flow encoding arterial spin tagging (FEAST). Magn. Reson. Med. 2003, 50, 599–607.
  40. Kim, T.; Kim, S.-G. 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, 333–342.
  41. Williams, D.S.; Detre, J.A.; Leigh, J.S.; Koretsky, A.P. Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc. Natl. Acad. Sci. USA 1992, 89, 212–216.
  42. Edelman, R.R.; Siewert, B.; Darby, D.G.; Thangaraj, V.; Nobre, A.C.; Mesulam, M.M.; Warach, S. Qualitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radio frequency. Radiology 1994, 192, 513–520.
  43. Silva, A.C.; Kim, S.G. Pseudo-continuous arterial spin labeling technique for measuring CBF dynamics with high temporal resolution. Magn. Reson. Med. 1999, 42, 425–429.
  44. Dai, W.; Garcia, D.; de Bazelaire, C.; Alsop, D.C. Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magn. Reson. Med. 2008, 60, 1488–1497.
  45. Silva, A.C.; Zhang, W.; Williams, D.S.; Koretsky, A.P. Multi-Slice MRI of Rat Brain Perfusion During Amphetamine Stimulation Using Arterial Spin Labeling. Magn. Reson. Med. 1995, 33, 209–214.
  46. Alsop, D.C.; Detre, J.A. Multisection cerebral blood flow MR imaging with continuous arterial spin labeling. Radiology 1998, 208, 410–416.
  47. Zhang, W.; Silva, A.C.; Williams, D.S.; Koretsky, A.P. NMR Measurement of Perfusion Using Arterial Spin Labeling Without Saturation of Macromolecular Spins. Magn. Reson. Med. 1995, 33, 370–376.
  48. Muir, E.R.; Shen, Q.; Duong, T.Q. Cerebral blood flow MRI in mice using the cardiac-spin-labeling technique. Magn. Reson. Med. 2008, 60, 744–748.
  49. Kim, S.-G. Quantification of relative cerebral blood flow change by flow-sensitive alternating inversion recovery (FAIR) technique: Application to functional mapping. Magn. Reson. Med. 1995, 34, 293–301.
  50. Wong, E.C.; Buxton, R.B.; Frank, L.R. Implementation of quantitative perfusion imaging techniques for functional brain mapping using pulsed arterial spin labeling. NMR Biomed. 1997, 10, 237–249.
  51. Chen, Y.; Wang, D.J.J.; Detre, J.A. Test-retest reliability of arterial spin labeling with common labeling strategies. J. Magn. Reson. Imaging 2011, 33, 940–949.
  52. Jahanian, H.; Noll, D.C.; Hernandez-Garcia, L. B0field inhomogeneity considerations in pseudo-continuous arterial spin labeling (pCASL): Effects on tagging efficiency and correction strategy. NMR Biomed. 2011, 24, 1202–1209.
  53. Alsop, D.C.; Detre, J.A.; Golay, X.; Günther, M.; Hendrikse, J.; Hernandez-Garcia, L.; Lu, H.; MacIntosh, B.J.; Parkes, L.M.; Smits, M.; et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn. Reson. Med. 2015, 73, 102–116.
  54. Hirschler, L.; Collomb, N.; Voiron, J.; Köhler, S.; Barbier, E.L.; Warnking, J.M. SAR comparison between CASL and pCASL at high magnetic field and evaluation of the benefit of a dedicated labeling coil. Magn. Reson. Med. 2020, 83, 254–261.
  55. Dobre, M.C.; Uğurbil, K.; Marjanska, M. Determination of blood longitudinal relaxation time (T1) at high magnetic field strengths. J. Magn. Reson. Imaging 2007, 25, 733–735.
  56. Franke, C.; van Dorsten, F.A.; Oláh, L.; Schwindt, W.; Hoehn, M. Arterial spin tagging perfusion imaging of rat brain: Dependency on magnetic field strength. J. Magn. Reson. Imaging 2000, 18, 1109–1113.
  57. Golay, X.; Petersen, E.T. Arterial Spin Labeling: Benefits and Pitfalls of High Magnetic Field. Neuroimaging Clin. North Am. 2006, 16, 259–268.
  58. St. Lawrence, K.S.; Wang, J. Effects of the apparent transverse relaxation time on cerebral blood flow measurements obtained by arterial spin labeling. Magn. Reson. Med. 2005, 53, 425–433.
  59. Maleki, N.; Dai, W.; Alsop, D.C. Optimization of background suppression for arterial spin labeling perfusion imaging. Magn. Reson. Mater. Physics Biol. Med. 2012, 25, 127–133.
  60. Shen, Q.; Duong, T.Q. Background suppression in arterial spin labeling MRI with a separate neck labeling coil. NMR Biomed. 2011, 24, 1111–1118.
  61. Damen, F.C.; Tain, R.-W.; Thomas, R.; Li, W.; Tai, L.; Cai, K. Evaluation of B0-correction of relative CBF maps using tagging distance dependent Z-spectrum (TADDZ). J. Magn. Reson. Imaging 2020, 65, 83–89.
  62. Berry, E.S.K.; Jezzard, P.; Okell, T.W. Off-resonance correction for pseudo-continuous arterial spin labeling using the optimized encoding scheme. NeuroImage 2019, 199, 304–312.
  63. Jahng, G.-H.; Weiner, M.W.; Schuff, N. Improved arterial spin labeling method: Applications for measurements of cerebral blood flow in human brain at high magnetic field MRI. Med Phys. 2007, 34, 4519–4525.
  64. Tanenbaum, A.B.; Snyder, A.Z.; Brier, M.R.; Ances, B.M. A Method for Reducing the Effects of Motion Contamination in Arterial Spin Labeling Magnetic Resonance Imaging. J. Cereb. Blood Flow Metab. 2015, 35, 1697–1702.
  65. Hébert, F.; Grand’Maison, M.; Ho, M.-K.; Lerch, J.P.; Hamel, E.; Bedell, B.J. Cortical atrophy and hypoperfusion in a transgenic mouse model of Alzheimer’s disease. Neurobiol. Aging 2013, 34, 1644–1652.
  66. Govaerts, K.; Lechat, B.; Struys, T.; Kremer, A.; Borghgraef, P.; van Leuven, F.; Himmelreich, U.; Dresselaers, T. Longitudinal assessment of cerebral perfusion and vascular response to hypoventilation in a bigenic mouse model of Alzheimer’s disease with amyloid and tau pathology. NMR Biomed. 2019, 32, e4037.
  67. Struys, T.; Govaerts, K.; Oosterlinck, W.; Casteels, C.; Bronckaers, A.; Koole, M.; van Laere, K.; Herijgers, P.; Lambrichts, I.; Himmelreich, U.; et al. In vivo evidence for long-term vascular remodeling resulting from chronic cerebral hypoperfusion in mice. J. Cereb. Blood Flow Metab. 2016, 37, 726–739.
  68. Holmes, H.E.; Colgan, N.; Ismail, O.; Ma, D.; Powell, N.M.; O’Callaghan, J.M.; Harrison, I.F.; Johnson, R.A.; Murray, T.K.; Ahmed, Z.; et al. Imaging the accumulation and suppression of tau pathology using multiparametric MRI. Neurobiol. Aging 2016, 39, 184–194.
  69. Oosterlinck, W.W.; Dresselaers, T.; Geldhof, V.; Van Santvoort, A.; Robberecht, W.; Herijgers, P.; Himmelreich, U. Response of mouse brain perfusion to hypo- and hyperventilation measured by arterial spin labeling. Magn. Reson. Med. 2011, 66, 802–811.
  70. Wong, E.C.; Cronin, M.; Wu, W.-C.; Inglis, B.; Frank, L.R.; Liu, T.T. Velocity-selective arterial spin labeling. Magn. Reson. Med. 2006, 55, 1334–1341.
  71. Le Bihan, D.; Breton, E.; Lallemand, D.; Aubin, M.L.; Vignaud, J.; Laval-Jeantet, M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988, 168, 497–505.
  72. Le Bihan, D. What can we see with IVIM MRI? NeuroImage 2019, 187, 56–67.
  73. Jensen, J.H.; Helpern, J.A.; Ramani, A.; Lu, H.; Kaczynski, K. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 2005, 53, 1432–1440.
  74. Jensen, J.H.; Helpern, J.A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 2010, 23, 698–710.
  75. Mulkern, R.V.; Haker, S.J.; Maier, S.E. On high b diffusion imaging in the human brain: Ruminations and experimental insights. J. Magn. Reson. Imaging 2009, 27, 1151–1162.
  76. Fournet, G.; Li, J.-R.; Cerjanic, A.M.; Sutton, B.P.; Ciobanu, L.; le Bihan, D. A two-pool model to describe the IVIM cerebral perfusion. J. Cereb. Blood Flow Metab. 2016, 37, 2987–3000.
  77. Kennan, R.P.; Gao, J.-H.; Zhong, J.; Gore, J.C. A general model of microcirculatory blood flow effects in gradient sensitized MRI. Med Phys. 1994, 21, 539–545.
  78. Wetscherek, A.; Stieltjes, B.; Laun, F.B. Flow-compensated intravoxel incoherent motion diffusion imaging. Magn. Reson. Med. 2014, 74, 410–419.
  79. Neil, J.J.; Scherrer, L.A.; Ackerman, J.J.H. An approach to solving the dynamic range problem in measurement of the pseudodilusion coefficient in vivo with spin echoes. J. Magn. Reson. 1991, 95, 607–614.
  80. Henkelman, R.M.; Neil, J.J.; Xiang, Q.-S. A quantitative interpretation of IVIM measurements of vascular perfusion in the rat brain. Magn. Reson. Med. 1994, 32, 464–469.
  81. Duong, T.Q.; Kim, S.-G. In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain. Magn. Reson. Med. 2000, 43, 393–402.
  82. Wurnig, M.C.; Donati, O.F.; Ulbrich, E.; Filli, L.; Kenkel, D.; Thoeny, H.C.; Boss, A. Systematic analysis of the intravoxel incoherent motion threshold separating perfusion and diffusion effects: Proposal of a standardized algorithm. Magn. Reson. Med. 2014, 74, 1414–1422.
  83. While, P.T. A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion-weighted MRI. Magn. Reson. Med. 2017, 78, 2373–2387.
  84. Li, Y.T.; Cercueil, J.-P.; Yuan, J.; Chen, W.; Loffroy, R.; Wáng, Y.X.J. Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: A comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation. Quant. Imaging Med. Surg. 2017, 7, 59–78.
  85. Ichikawa, S.; Motosugi, U.; Ichikawa, T.; Sano, K.; Morisaka, H.; Araki, T. Intravoxel incoherent motion imaging of the kidney: Alterations in diffusion and perfusion in patients with renal dysfunction. J. Magn. Reson. Imaging 2013, 31, 414–417.
  86. Schneider, M.J.; Dietrich, O.; Ingrisch, M.; Helck, A.; Winter, K.S.; Reiser, M.F.; Staehler, M.; Casuscelli, J.; Notohamiprodjo, M. Intravoxel Incoherent Motion Magnetic Resonance Imaging in Partially Nephrectomized Kidneys. Investig. Radiol. 2016, 51, 323–330.
  87. Klauß, M.; Maier-Hein, K.; Tjaden, C.; Hackert, T.; Grenacher, L.; Stieltjes, B. IVIM DW-MRI of autoimmune pancreatitis: Therapy monitoring and differentiation from pancreatic cancer. Eur. Radiol. 2016, 26, 2099–2106.
  88. Kang, K.M.; Lee, J.M.; Yoon, J.H.; Kiefer, B.; Han, J.K.; Choi, B.I. Intravoxel Incoherent Motion Diffusion-weighted MR Imaging for Characterization of Focal Pancreatic Lesions. Radiology 2014, 270, 444–453.
  89. Zhang, X.; Ingo, C.; Teeuwisse, W.M.; Chen, Z.; van Osch, M.J.P. Comparison of perfusion signal acquired by arterial spin labeling-prepared intravoxel incoherent motion (IVIM) MRI and conventional IVIM MRI to unravel the origin of the IVIM signal. Magn. Reson. Med. 2018, 79, 723–729.
  90. Paschoal, A.M.; Leoni, R.F.; dos Santos, A.C.; Paiva, F.F. Intravoxel incoherent motion MRI in neurological and cerebrovascular diseases. NeuroImage Clin. 2018, 20, 705–714.
  91. Jalnefjord, O.; Montelius, M.; Starck, G.; Ljungberg, M. Optimization of b-value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting. Magn. Reson. Med. 2019, 82, 1541–1552.
  92. Perucho, J.A.U.; Chang, H.C.C.; Vardhanabhuti, V.; Wang, M.; Becker, A.S.; Wurnig, M.C.; Lee, E.Y.P. B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer. Korean J. Radiol. 2020, 21, 218–227.
  93. Lu, H.; Golay, X.; Pekar, J.J.; van Zijl, P.C. Functional magnetic resonance imaging based on changes in vascular space occupancy. Magn. Reson. Med. 2003, 50, 263–274.
  94. Lu, H.; Hua, J.; van Zijl, P.C.M. Noninvasive functional imaging of cerebral blood volume with vascular-space-occupancy (VASO) MRI. NMR Biomed. 2013, 26, 932–948.
  95. Hua, J.; Jones, C.K.; Qin, Q.; van Zijl, P.C.M. Implementation of vascular-space-occupancy MRI at 7T. Magn. Reson. Med. 2013, 69, 1003–1013.
  96. Hua, J.; Donahue, M.J.; Zhao, J.M.; Grgac, K.; Huang, A.J.; Zhou, J.; van Zijl, P.C.M. Magnetization transfer enhanced vascular-space-occupancy (MT-VASO) functional MRI. Magn. Reson. Med. 2009, 61, 944–951.
  97. Huber, L.; Ivanov, D.; Krieger, S.N.; Streicher, M.N.; Mildner, T.; Poser, B.A.; Möller, H.E.; Turner, R. Slab-selective, BOLD-corrected VASO at 7 Tesla provides measures of cerebral blood volume reactivity with high signal-to-noise ratio. Magn. Reson. Med. 2014, 72, 137–148.
  98. Jin, T.; Kim, S.-G. Improved cortical-layer specificity of vascular space occupancy fMRI with slab inversion relative to spin-echo BOLD at 9.4 T. NeuroImage 2008, 40, 59–67.
  99. Lu, H.; Law, M.; Johnson, G.; Ge, Y.; van Zijl, P.C.M.; Helpern, J.A. Novel approach to the measurement of absolute cerebral blood volume using vascular-space-occupancy magnetic resonance imaging. Magn. Reson. Med. 2005, 54, 1403–1411.
  100. Hua, J.; Qin, Q.; Donahue, M.J.; Zhou, J.; Pekar, J.J.; van Zijl, P.C.M. Inflow-based vascular-space-occupancy (iVASO) MRI. Magn. Reson. Med. 2011, 66, 40–56.
  101. Donahue, M.J.; Strother, M.K.; Hendrikse, J. Novel MRI Approaches for Assessing Cerebral Hemodynamics in Ischemic Cerebrovascular Disease. Stroke 2012, 43, 903–915.
  102. Donahue, M.J.; Sideso, E.; MacIntosh, B.J.; Kennedy, J.; Handa, A.; Jezzard, P. Absolute Arterial Cerebral Blood Volume Quantification Using Inflow Vascular-Space-Occupancy with Dynamic Subtraction Magnetic Resonance Imaging. J. Cereb. Blood Flow Metab. 2010, 30, 1329–1342.
  103. Hua, J.; Qin, Q.; Pekar, J.J.; van Zijl, P.C.M. Measurement of absolute arterial cerebral blood volume in human brain without using a contrast agent. NMR Biomed. 2011, 24, 1313–1325.
  104. Kim, S.-G.; Harel, N.; Jin, T.; Kim, T.; Lee, P.; Zhao, F. Cerebral blood volume MRI with intravascular superparamagnetic iron oxide nanoparticles. NMR Biomed. 2012, 26, 949–962.
  105. Mandeville, J.B.; Marota, J.J.A. Vascular filters of functional MRI: Spatial localization using BOLD and CBV contrast. Magn. Reson. Med. 1999, 42, 591–598.
  106. Le Duc, G.; Péoc’h, M.; Rémy, C.; Charpy, O.; Muller, R.N.; Le Bas, J.F.; Décorps, M. Use of T2-weighted susceptibility contrast MRI for mapping the blood volume in the glioma-bearing rat brain. Magn. Reson. Med. 1999, 42, 754–761.
  107. Weinstein, J.S.; Varallyay, C.G.; Dosa, E.; Gahramanov, S.; Hamilton, B.; Rooney, W.D.; Muldoon, L.L.; Neuwelt, E.A. 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.
  108. Troprès, I.; Pannetier, N.; Grand, S.; Lemasson, B.; Moisan, A.; Peoc’h, M.; Rémy, C.; Barbier, E.L. Imaging the microvessel caliber and density: Principles and applications of microvascular MRI. J. Cereb. Blood Flow Metab. 2015, 73, 325–341.
  109. Boxerman, J.L.; Bandettini, P.A.; Kwong, K.K.; Baker, J.R.; Davis, T.L.; Rosen, B.R.; Weisskoff, R.M. The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo. Magn. Reson. Med. 1995, 34, 4–10.
  110. Jensen, J.H.; Chandra, R. MR imaging of microvasculature. Magn. Reson. Med. 2000, 44, 224–230.
  111. Troprès, I.; Grimault, S.; Vaeth, A.; Grillon, E.; Julien, C.; Payen, J.-F.; Lamalle, L.; Décorps, M. Vessel size imaging. Magn. Reson. Med. 2001, 45, 397–408.
  112. Kiselev, V.G.; Strecker, R.; Ziyeh, S.; Speck, O.; Hennig, J. Vessel size imaging in humans. Magn. Reson. Med. 2005, 53, 553–563.
  113. Hsu, Y.-Y.; Yang, W.-S.; Lim, K.-E.; Liu, H.-L. Vessel size imaging using dual contrast agent injections. J. Magn. Reson. Imaging 2009, 30, 1078–1084.
  114. Schmiedeskamp, H.; Straka, M.; Bammer, R. Compensation of slice profile mismatch in combined spin- and gradient-echo echo-planar imaging pulse sequences. Magn. Reson. Med. 2012, 67, 378–388.
  115. Skinner, J.T.; Robison, R.K.; Elder, C.P.; Newton, A.T.; Damon, B.M.; Quarles, C.C. Evaluation of a multiple spin- and gradient-echo (SAGE) EPI acquisition with SENSE acceleration: Applications for perfusion imaging in and outside the brain.J. Magn. Reson. Imaging 2014, 32, 1171–1180.
  116. Van Vaals, J.J.; Brummer, M.E.; Dixon, W.T.; Tuithof, H.H.; Engels, H.; Nelson, R.C.; Gerety, B.M.; Chezmar, J.L.; Boer, J.A.D. “Keyhole” method for accelerating imaging of contrast agent uptake. J. Magn. Reson. Imaging 1993, 3, 671–675.
  117. Han, M.; Yang, B.; Fernandez, B.; Lafontaine, M.; Alcaide-Leon, P.; Jakary, A.; Burns, B.L.; Morrison, M.A.; Villanueva-Meyer, J.E.; Chang, S.M.; et al. Simultaneous multi-slice spin- and gradient-echo dynamic susceptibility-contrast perfusion-weighted MRI of gliomas. NMR Biomed. 2020, 34, e4399.
  118. Wymer, D.T.; Patel, K.P.; Burke, W.F., III; Bhatia, V.K. Phase-Contrast MRI: Physics, Techniques, and Clinical Applications. Radiographics 2020, 40, 122–140.
  119. Korbecki, A.; Zimny, A.; Podgórski, P.; Sąsiadek, M.; Bladowska, J. Imaging of cerebrospinal fluid flow: Fundamentals, techniques, and clinical applications of phase-contrast magnetic resonance imaging. Pol. J. Radiol. 2019, 84, e240–e250.
  120. Srichai, M.B.; Lim, R.P.; Wong, S.; Lee, V.S. Cardiovascular Applications of Phase-Contrast MRI. Am. J. Roentgenol. 2009, 192, 662–675.
  121. Nett, E.J.; Johnson, K.M.; Frydrychowicz, A.; del Rio, A.M.; Schrauben, E.; Francois, C.J.; Wieben, O. Four-dimensional phase contrast MRI with accelerated dual velocity encoding. J. Magn. Reson. Imaging 2012, 35, 1462–1471.
  122. Battal, B.; Kocaoglu, M.; Bulakbasi, N.; Husmen, G.; Sanal, H.T.; Tayfun, C. Cerebrospinal fluid flow imaging by using phase-contrast MR technique. Br. J. Radiol. 2011, 84, 758–765.
  123. Guo, G. The Quantification of Cerebral Blood Flow by Phase Contrast MRA: Basics and Applications. Neuroradiol. J. 2008, 21, 11–21.
  124. Saloner, D. The AAPM/RSNA physics tutorial for residents. An introduction to MR angiography. Radiographics 1995, 15, 453–465.
  125. Frydrychowicz, A.; François, C.J.; Turski, P.A. Four-dimensional phase contrast magnetic resonance angiography: Potential clinical applications. Eur. J. Radiol. 2011, 80, 24–35.
  126. Markl, M.; Frydrychowicz, A.; Kozerke, S.; Hope, M.; Wieben, O. 4D flow MRI. J. Magn. Reson. Imaging 2012, 36, 1015–1036.
  127. Spilt, A.; Box, F.M.A.; van der Geest, R.J.; Reiber, J.H.C.; Kunz, P.; Kamper, A.M.; Blauw, G.J.; van Buchem, M.A. Reproducibility of total cerebral blood flow measurements using phase contrast magnetic resonance imaging. J. Magn. Reson. Imaging 2002, 16, 1–5.
  128. Wei, Z.; Chen, L.; Lin, Z.; Jiang, D.; Xu, J.; Liu, P.; van Zijl, P.C.M.; Lu, H. Optimization of phase-contrast MRI for the estimation of global cerebral blood flow of mice at 11.7T. Magn. Reson. Med. 2019, 81, 2566–2575.
  129. Chiu, S.-C.; Hsu, S.-T.; Huang, C.-W.; Shen, W.-C.; Peng, S.-L. Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery. J. Vis. Exp. 2018, e57304.
  130. Peng, S.-L.; Su, P.; Wang, F.-N.; Cao, Y.; Zhang, R.; Lu, H.; Liu, P. Optimization of phase-contrast MRI for the quantification of whole-brain cerebral blood flow. J. Magn. Reson. Imaging 2015, 42, 1126–1133.
  131. Enzmann, D.R.; Marks, M.P.; Pelc, N.J. Comparison of cerebral artery blood flow measurements with gated cine and ungated phase-contrast techniques. J. Magn. Reson. Imaging 1993, 3, 705–712.
  132. Hofman, M.B.M.; Kouwenhoven, M.; Sprenger, M.; van Rossum, A.C.; Valk, J.; Westerhof, N. Nontriggered magnetic resonance velocity measurement of the time-average of pulsatile velocity. Magn. Reson. Med. 1993, 29, 648–655.
  133. Bakker, C.J.G.; Hartkamp, M.J.; Mali, W.P.T.M. Measuring blood flow by nontriggered 2D phase-contrast MR angiography. J. Magn. Reson. Imaging 1996, 14, 609–614.
  134. Bonekamp, D.; Degaonkar, M.; Barker, P.B. Quantitative cerebral blood flow in dynamic susceptibility contrast MRI using total cerebral flow from phase contrast magnetic resonance angiography. Magn. Reson. Med. 2011, 66, 57–66.
  135. Aslan, S.; Xu, F.; Wang, P.L.; Uh, J.; Yezhuvath, U.S.; van Osch, M.; Lu, H. Estimation of labeling efficiency in pseudocontinuous arterial spin labeling. Magn. Reson. Med. 2010, 63, 765–771.
  136. Liu, P.; Lu, H.; Filbey, F.M.; Pinkham, A.E.; McAdams, C.J.; Adinoff, B.; Daliparthi, V.; Cao, Y. Automatic and Reproducible Positioning of Phase-Contrast MRI for the Quantification of Global Cerebral Blood Flow. PLoS ONE 2014, 9, e95721.
  137. Liu, P.; Huang, H.; Rollins, N.; Chalak, L.F.; Jeon, T.; Halovanic, C.; Lu, H. Quantitative assessment of global cerebral metabolic rate of oxygen (CMRO2) in neonates using MRI. NMR Biomed. 2014, 27, 332–340.
  138. Xu, F.; Ge, Y.; Lu, H. Noninvasive quantification of whole-brain cerebral metabolic rate of oxygen (CMRO2) by MRI. Magn. Reson. Med. 2009, 62, 141–148.
  139. Haacke, E.M.; Mittal, S.; Wu, Z.; Neelavalli, J.; Cheng, Y.-C.N. Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1. Am. J. Neuroradiol. 2009, 30, 19–30.
  140. Liu, S.; Buch, S.; Chen, Y.; Choi, H.-S.; Dai, Y.; Habib, C.; Hu, J.; Jung, J.-Y.; Luo, Y.; Utriainen, D.; et al. Susceptibility-weighted imaging: Current status and future directions. NMR Biomed. 2017, 30, e3552.
  141. Nissi, M.J.; Tóth, F.; Wang, L.; Carlson, C.S.; Ellermann, J.M. Improved Visualization of Cartilage Canals Using Quantitative Susceptibility Mapping. PLoS ONE 2015, 10, e0132167.
  142. Jenkinson, M. Fast, automated, N-dimensional phase-unwrapping algorithm. Magn. Reson. Med. 2002, 49, 193–197.
  143. Li, W.; Wu, B.; Liu, C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. NeuroImage 2011, 55, 1645–1656.
  144. Liu, T.; Khalidov, I.; de Rochefort, L.; Spincemaille, P.; Liu, J.; Tsiouris, A.J.; Wang, Y. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed. 2011, 24, 1129–1136.
  145. Schweser, F.; Deistung, A.; Lehr, B.W.; Reichenbach, J.R. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? NeuroImage 2011, 54, 2789–2807.
  146. Li, W.; Avram, A.V.; Wu, B.; Xiao, X.; Liu, C. Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR Biomed. 2014, 27, 219–227.
  147. Liu, C.; Li, W.; Tong, K.A.; Yeom, K.W.; Kuzminski, S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J. Magn. Reson. Imaging 2015, 42, 23–41.
  148. Liu, C.; Wei, H.; Gong, N.-J.; Cronin, M.; Dibb, R.; Decker, K. Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications. Tomography 2015, 1, 3–17.
  149. Bollmann, S.; Rasmussen, K.G.B.; Kristensen, M.; Blendal, R.G.; Østergaard, L.R.; Plocharski, M.; O’Brien, K.; Langkammer, C.; Janke, A.; Barth, M. DeepQSM—Using deep learning to solve the dipole inversion for quantitative susceptibility mapping. NeuroImage 2019, 195, 373–383.
  150. Wang, Y.; Liu, T. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magn. Reson. Med. 2015, 73, 82–101.
  151. Liu, T.; Spincemaille, P.; de Rochefort, L.; Kressler, B.; Wang, Y. Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn. Reson. Med. 2008, 61, 196–204.
  152. Vinayagamani, S.; Sheelakumari, R.; Sabarish, S.; Senthilvelan, S.; Ros, R.; Thomas, B.; Kesavadas, C. Quantitative Susceptibility Mapping: Technical Considerations and Clinical Applications in Neuroimaging. J. Magn. Reson. Imaging 2021, 53, 23–37.
  153. Li, W.; Wang, N.; Yu, F.; Han, H.; Cao, W.; Romero, R.; Tantiwongkosi, B.; Duong, T.Q.; Liu, C. A method for estimating and removing streaking artifacts in quantitative susceptibility mapping. NeuroImage 2015, 108, 111–122.
  154. Chatnuntawech, I.; McDaniel, P.; Cauley, S.F.; Gagoski, B.A.; Langkammer, C.; Martin, A.; Grant, P.E.; Wald, L.L.; Setsompop, K.; Adalsteinsson, E.; et al. Single-step quantitative susceptibility mapping with variational penalties. NMR Biomed. 2017, 30, e3570.
  155. Liu, J.; Liu, T.; de Rochefort, L.; Ledoux, J.; Khalidov, I.; Chen, W.; Tsiouris, A.J.; Wisnieff, C.; Spincemaille, P.; Prince, M.R.; et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. NeuroImage 2012, 59, 2560–2568.
  156. Wharton, S.; Schäfer, A.; Bowtell, R. Susceptibility mapping in the human brain using threshold-based k-space division. Magn. Reson. Med. 2010, 63, 1292–1304.
  157. Birkl, C.; Langkammer, C.; Sati, P.; Enzinger, C.; Fazekas, F.; Ropele, S. Quantitative Susceptibility Mapping to Assess Cerebral Vascular Compliance. Am. J. Neuroradiol. 2019, 40, 460–463.
  158. Liu, C. Susceptibility tensor imaging. Magn. Reson. Med. 2010, 63, 1471–1477.
  159. Li, W.; Liu, C.; Duong, T.Q.; van Zijl, P.C.M.; Li, X. Susceptibility tensor imaging (STI) of the brain. NMR Biomed. 2017, 30, e3540.
  160. Deistung, A.; Schweser, F.; Reichenbach, J.R. Overview of quantitative susceptibility mapping. NMR Biomed. 2017, 30, e3569.
  161. Reichenbach, J.R.; Schweser, F.; Serres, B.; Deistung, A. Quantitative Susceptibility Mapping: Concepts and Applications. Clin. Neuroradiol. 2015, 25, 225–230.
  162. Hsieh, M.-C.; Kuo, L.-W.; Huang, Y.-A.; Chen, J.-H. Investigating hyperoxic effects in the rat brain using quantitative susceptibility mapping based on MRI phase. Magn. Reson. Med. 2017, 77, 592–602.
  163. Hsieh, M.-C.; Tsai, C.-Y.; Liao, M.-C.; Yang, J.-L.; Su, C.-H.; Chen, J.-H. Quantitative Susceptibility Mapping-Based Microscopy of Magnetic Resonance Venography (QSM-mMRV) for In Vivo Morphologically and Functionally Assessing Cerebromicrovasculature in Rat Stroke Model. PLoS ONE 2016, 11, e0149602.
  164. Wei, H.; Xie, L.; Dibb, R.; Li, W.; Decker, K.; Zhang, Y.; Johnson, G.A.; Liu, C. Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping. NeuroImage 2016, 137, 107–115.
  165. Ogawa, S.; Lee, T.M.; Kay, A.R.; Tank, D.W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. USA 1990, 87, 9868–9872.
  166. Jonckers, E.; Shah, D.; Hamaide, J.; Verhoye, M.; van der Linden, A. The power of using functional fMRI on small rodents to study brain pharmacology and disease. Front. Pharmacol. 2015, 6, 231.
  167. Pan, W.-J.; Billings, J.C.W.; Grooms, J.K.; Shakil, S.; Keilholz, S.D. Considerations for resting state functional MRI and functional connectivity studies in rodents. Front. Neurosci. 2015, 9, 269.
  168. He, X.; Yablonskiy, D.A. Quantitative BOLD: Mapping of human cerebral deoxygenated blood volume and oxygen extraction fraction: Default state. Magn. Reson. Med. 2006, 57, 115–126.
  169. Yablonskiy, D.A.; Haacke, E.M. Theory of NMR signal behavior in magnetically inhomogeneous tissues: The static dephasing regime. Magn. Reson. Med. 1994, 32, 749–763.
  170. Yablonskiy, D.A. Quantitation of intrinsic magnetic susceptibility-related effects in a tissue matrix. Phantom study. Magn. Reson. Med. 1998, 39, 417–428.
  171. He, X.; Zhu, M.; Yablonskiy, D.A. Validation of oxygen extraction fraction measurement by qBOLD technique. Magn. Reson. Med. 2008, 60, 882–888.
  172. Stone, A.J.; Blockley, N.P. A streamlined acquisition for mapping baseline brain oxygenation using quantitative BOLD. NeuroImage 2017, 147, 79–88.
  173. Yin, Y.; Zhang, Y.; Gao, J.-H. Dynamic measurement of oxygen extraction fraction using a multiecho asymmetric spin echo (MASE) pulse sequence. Magn. Reson. Med. 2018, 80, 1118–1124.
  174. Cho, J.; Kee, Y.; Spincemaille, P.; Nguyen, T.D.; Zhang, J.; Gupta, A.; Zhang, S.; Wang, Y. Cerebral metabolic rate of oxygen (CMRO2) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD). Magn. Reson. Med. 2018, 80, 1595–1604.
More