Skip to main content

Susceptibility Weighted Imaging

  • Chapter
  • First Online:
Neuroimaging Techniques in Clinical Practice
  • 1031 Accesses

Abstract

Susceptibility-weighted MRI (SWI) offers unique and complementary information to the conventional contrasts typically used in magnetic resonance imaging (MRI). It uses both magnitude and phase information of high-resolution gradient-echo-based sequences, which is then further post-processed to improve vascular conspicuity and/or tissue contrasts due to the presence of susceptibility sources. SWI has become an established widely used clinical tool, particularly for neuroimaging and imaging vascular pathologies. It is a qualitative technique that is limited by the orientation-dependent and nonlocal nature of the phase and is unable to distinguish unambiguously between positive and negative magnetic susceptibilities. The more recent development of quantitative susceptibility mapping (QSM) overcomes these limitations and allows the generation of three-dimensional maps showing the variation of the relative magnetic susceptibility within the human brain. This chapter provides a background of the SWI method and reviews current research and clinical applications. It also highlights some of the limitations and outlines potential future directions of the technique.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med. 2004;52(3):612–8.

    PubMed  Google Scholar 

  2. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng YC. Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol. 2009;30:19–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Haacke EM, Reichenbach JR, editors. Susceptibility weighted imaging in MRI. Basic concepts and clinical applications. Hoboken, NJ: Wiley-Blackwell; 2011, 743 pp.

    Google Scholar 

  4. Liu S, Buch S, Chen Y, Choi HS, Dai Y, Habib C, Hu J, Jung JY, Luo Y, Utriainen D, Wang M, Wu D, Xia S, Haacke EM. Susceptibility-weighted imaging: current status and future directions. NMR Biomed. 2017;30(4):e3552. https://doi.org/10.1002/nbm.3552.

    Article  CAS  Google Scholar 

  5. Reichenbach JR, Venkatesan R, Yablonskiy DA, Thompson MR, Lai S, Haacke EM. Theory and application of static field inhomogeneity effects in gradient-echo imaging. J Magn Reson Imaging. 1997;7(2):266–79.

    CAS  PubMed  Google Scholar 

  6. Heyn C, Alcaide-Leon P, Bharatha A, Sussman MS, Kucharczyk W, Mandell DM. Susceptibility-weighted imaging in neurovascular disease. Top Magn Reson Imaging. 2016;25(2):63–71.

    PubMed  Google Scholar 

  7. Robinson RJ, Bhuta S. Susceptibility-weighted imaging of the brain: current utility and potential applications. J Neuroimaging. 2011;21(4):e189–204.

    PubMed  Google Scholar 

  8. Sehgal V, Delproposto Z, Haacke EM, Tong KA, Wycliffe N, Kido DK, Xu Y, Neelavalli J, Haddar D, Reichenbach JR. Clinical applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson Imaging. 2005;22(4):439–50.

    PubMed  Google Scholar 

  9. Sehgal V, Delproposto Z, Haddar D, Haacke EM, Sloan AE, Zamorano LJ, Barger G, Hu J, Xu Y, Prabhakaran KP, Elangovan IR, Neelavalli J, Reichenbach JR. Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J Magn Reson Imaging. 2006;24(1):41–51.

    PubMed  Google Scholar 

  10. Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology. 1997;204(1):272–7.

    CAS  PubMed  Google Scholar 

  11. Krishnan AS, Lansley JA, Jäger HR, Mankad K. New vistas in clinical practice: susceptibility-weighted imaging. Quant Imaging Med Surg. 2015;5(3):448–52.

    PubMed  PubMed Central  Google Scholar 

  12. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. AJNR Am J Neuroradiol. 2009;30:232–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Skalski KA, Kessler AT, Bhatt AA. Hemorrhagic and non-hemorrhagic causes of signal loss on susceptibility-weighted imaging. Emerg Radiol. 2018;25(6):691–701.

    PubMed  Google Scholar 

  14. Thomas B, Somasundaram S, Thamburaj K, Kesavadas C, Gupta AK, Bodhey NK, Kapilamoorthy TR. Clinical applications of susceptibility weighted MR imaging of the brain—a pictorial review. Neuroradiology. 2008;50(2):105–16.

    PubMed  Google Scholar 

  15. Schenck JF. The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys. 1996;23(6):815–50.

    CAS  PubMed  Google Scholar 

  16. Duyn JH, Schenck J. Contributions to magnetic susceptibility of brain tissue. NMR Biomed. 2017;30(4):e3546. https://doi.org/10.1002/nbm.3546.

    Article  Google Scholar 

  17. Chu SC, Xu Y, Balschi JA, Springer CS Jr. Bulk magnetic susceptibility shifts in NMR studies of compartmentalized samples: use of paramagnetic reagents. Magn Reson Med. 1990;13(2):239–62.

    CAS  PubMed  Google Scholar 

  18. Hagberg GE, Welch EB, Greiser A. The sign convention for phase values on different vendor systems: definition and implications for susceptibility-weighted imaging. Magn Reson Imaging. 2010;28(2):297–300.

    PubMed  Google Scholar 

  19. Rauscher A, Sedlacik J, Deistung A, Mentzel HJ, Reichenbach JR. Susceptibility weighted imaging: data acquisition, image reconstruction and clinical applications. Z Med Phys. 2006;16:240–50.

    PubMed  Google Scholar 

  20. Denk C, Rauscher A. Susceptibility weighted imaging with multiple echoes. J Magn Reson Imaging. 2010;31(1):185–91.

    PubMed  Google Scholar 

  21. Liu C, Li W, Tong KA, Yeom KW, Kuzminski S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging. 2015;42(1):23–41.

    PubMed  Google Scholar 

  22. Deistung A, Dittrich E, Sedlacik J, Rauscher A, Reichenbach JR. ToF-SWI: simultaneous time of flight and fully flow compensated susceptibility weighted imaging. J Magn Reson Imaging. 2009;29(6):1478–84.

    PubMed  Google Scholar 

  23. Xu Y, Haacke EM. The role of voxel aspect ratio in determining apparent vascular phase behavior in susceptibility weighted imaging. Magn Reson Imaging. 2006;24(2):155–60.

    CAS  PubMed  Google Scholar 

  24. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y. Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging. 2015;33(1):1–25.

    PubMed  Google Scholar 

  25. Halefoglu AM, Yousem DM. Susceptibility weighted imaging: clinical applications and future directions. World J Radiol. 2018;10(4):30–45.

    PubMed  PubMed Central  Google Scholar 

  26. Du YP, Jin Z. Simultaneous acquisition of MR angiography and venography (MRAV). Magn Reson Med. 2008;59(5):954–8.

    PubMed  Google Scholar 

  27. Ye H, Hu J, Wu D, Haacke EM. Noncontrast-enhanced magnetic resonance angiography and venography imaging with enhanced angiography. J Magn Reson Imaging. 2013;38(6):1539–48.

    PubMed  Google Scholar 

  28. Deistung A. Susceptibility weighted imaging and quantitative susceptibility mapping at 3 Tesla and beyond. PhD Thesis, Technical University Ilmenau. 2013.

    Google Scholar 

  29. Martínez-Santiesteban FM, Swanson SD, Noll DC, Anderson DJ. Object orientation independence of susceptibility weighted imaging by using a sigmoid-type phase window. Proc Int Soc Mag Reson Med. 2006;14:2399.

    Google Scholar 

  30. Brainovich V, Sabatini U, Hagberg GE. Advantages of using multiple-echo image combination and asymmetric triangular phase masking in magnetic resonance venography at 3 T. Magn Reson Imaging. 2009;27(1):23–37.

    PubMed  Google Scholar 

  31. Casciaro S, Bianco R, Franchini R, Casciaro E, Conversano F. A new automatic phase mask filter for high-resolution brain venography at 3 T: theoretical background and experimental validation. Magn Reson Imaging. 2010;28(4):511–9.

    PubMed  Google Scholar 

  32. Quinn MP, Gati JS, Klassen LM, Lin AW, Bird JR, Leung SE, Menon RS. Comparison of multiecho postprocessing schemes for SWI with use of linear and nonlinear mask functions. AJNR Am J Neuroradiol. 2014;35(1):38–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Reichenbach JR, Essig M, Haacke EM, Lee BC, Przetak C, Kaiser WA, Schad LR. High-resolution venography of the brain using magnetic resonance imaging. MAGMA. 1998;6(1):62–9.

    CAS  PubMed  Google Scholar 

  34. Deistung A, Rauscher A, Sedlacik J, Stadler J, Witoszynskyj S, Reichenbach JR. Susceptibility weighted imaging at ultrahigh magnetic field strengths: theoretical considerations and experimental results. Magn Reson Med. 2008;60(5):1155–68.

    PubMed  Google Scholar 

  35. Duyn JH. MR susceptibility imaging. J Magn Reson. 2013;229:198–207.

    CAS  PubMed  Google Scholar 

  36. Reichenbach JR, Barth M, Haacke EM, Klarhöfer M, Kaiser WA, Moser E. High-resolution MR venography at 3.0 Tesla. J Comput Assist Tomogr. 2000;24(6):949–57.

    CAS  PubMed  Google Scholar 

  37. Ladd ME, Bachert P, Meyerspeer M, Moser E, Nagel AM, Norris DG, Schmitter S, Speck O, Straub S, Zaiss M. Pros and cons of ultra-high-field MRI/MRS for human application. Prog Nucl Magn Reson Spectrosc. 2018;109:1–50.

    CAS  PubMed  Google Scholar 

  38. Ge Y, Barnes S, Heller S, Sodickson DK, Tang L, Haacke EM, Dai J, Grossman RI. Three-dimensional high resolution venography using susceptibility weighted imaging at 7 T. Chin J Magn Reson Imaging. 2010;1(2):83–93.

    Google Scholar 

  39. Geißler A, Fischmeister FP, Grabner G, Wurnig M, Rath J, Foki T, Matt E, Trattnig S, Beisteiner R, Robinson SD. Comparing the microvascular specificity of the 3- and 7-T BOLD response using ICA and susceptibility-weighted imaging. Front Hum Neurosci. 2013;7:474.

    PubMed  PubMed Central  Google Scholar 

  40. Koopmans PJ, Manniesing R, Niessen WJ, Viergever MA, Barth M. MR venography of the human brain using susceptibility weighted imaging at very high field strength. MAGMA. 2008;21(1–2):149–58.

    PubMed  Google Scholar 

  41. Liu S, Brisset JC, Hu J, Haacke EM, Ge Y. Susceptibility weighted imaging and quantitative susceptibility mapping of the cerebral vasculature using ferumoxytol. J Magn Reson Imaging. 2018;47(3):621–33.

    PubMed  Google Scholar 

  42. Moser E, Stahlberg F, Ladd ME, Trattnig S. 7-T MR—from research to clinical applications? NMR Biomed. 2012;25(5):695–716.

    PubMed  Google Scholar 

  43. Hsu CC, Kwan GNC, Hapugoda S, Craigie M, Watkins TW, Haacke EM. Susceptibility weighted imaging in acute cerebral ischemia: review of emerging technical concepts and clinical applications. Neuroradiol J. 2017;30(2):109–19.

    PubMed  PubMed Central  Google Scholar 

  44. Radbruch A, Mucke J, Schweser F, Deistung A, Ringleb PA, Ziener CH, Roethke M, Schlemmer HP, Heiland S, Reichenbach JR, Bendszus M, Rohde S. Comparison of susceptibility weighted imaging and TOF-angiography for the detection of thrombi in acute stroke. PLoS One. 2013;8(5):e63459.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Weisstanner C, Gratz PP, Schroth G, Verma RK, Köchl A, Jung S, Arnold M, Gralla J, Zubler C, Hsieh K, Mordasini P, El-Koussy M. Thrombus imaging in acute stroke: correlation of thrombus length on susceptibility-weighted imaging with endovascular reperfusion success. Eur Radiol. 2014;24(8):1735–41.

    PubMed  PubMed Central  Google Scholar 

  46. Chen CY, Chen CI, Tsai FY, Tsai PH, Chan WP. Prominent vessel sign on susceptibility-weighted imaging in acute stroke: prediction of infarct growth and clinical outcome. PLoS One. 2015;10(6):e0131118.

    PubMed  PubMed Central  Google Scholar 

  47. Xia S, Utriainen D, Tang J, Kou Z, Zheng G, Wang X, Shen W, Haacke EM, Lu G. Decreased oxygen saturation in asymmetrically prominent cortical veins in patients with cerebral ischemic stroke. Magn Reson Imaging. 2014;32(10):1272–6.

    PubMed  Google Scholar 

  48. Yuan T, Ren G, Quan G, Gao D. Fewer peripheral asymmetrical cortical veins is a predictor of favorable outcome in MCA infarctions with SWI-DWI mismatch. J Magn Reson Imaging. 2018;48(4):964–70.

    Google Scholar 

  49. Nighoghossian N, Hermier M, Adeleine P, Blanc-Lasserre K, Derex L, Honnorat J, Philippeau F, Dugor JF, Froment JC, Trouillas P. Old microbleeds are a potential risk factor for cerebral bleeding after ischemic stroke: a gradient-echo T2∗-weighted brain MRI study. Stroke. 2002;33(3):735–42.

    CAS  PubMed  Google Scholar 

  50. Annan M, Gaudron M, Cottier JP, Cazals X, Dejobert M, Corcia P, Bertrand P, Mondon K, de Toffol B, Debiais S. Functional outcome of hemorrhagic transformation after thrombolysis for ischemic stroke: a prospective study. Cerebrovasc Dis Extra. 2015;5(3):103–6.

    PubMed  PubMed Central  Google Scholar 

  51. Lu J, Li YH, Li YD, Li MH, Zhao JG, Chen SW. The clinical value of antiplatelet therapy for patients with hemorrhage after thrombolysis based on susceptibility-weighted imaging: a prospective pilot study. Eur J Radiol. 2012;81(12):4094–8.

    PubMed  Google Scholar 

  52. Mane RS, Gowda AK, Kamte SG, Mohan B, Hedna V, Zohra F, Krishnamurthy U, Kumar AA. Should susceptibility-weighted imaging be included in the protocol for evaluation of acute ischemic stroke patients? West Afr J Radiol. 2016;23:59–63.

    Google Scholar 

  53. Verclytte S, Fisch O, Colas L, Vanaerde O, Toledano M, Budzik JF. ASL and susceptibility-weighted imaging contribution to the management of acute ischaemic stroke. Insights Imaging. 2017;8(1):91–100.

    PubMed  Google Scholar 

  54. Park MG, Yoon CH, Baik SK, Park KP. Susceptibility vessel sign for intra-arterial thrombus in acute posterior cerebral artery infarction. J Stroke Cerebrovasc Dis. 2015;24(6):1229–34.

    PubMed  Google Scholar 

  55. Park MG, Oh SJ, Baik SK, Jung DS, Park KP. Susceptibility-weighted imaging for detection of thrombus in acute cardioembolic stroke. J Stroke. 2016;18(1):73–9.

    PubMed  PubMed Central  Google Scholar 

  56. Payabvash S, Benson JC, Taleb S, Rykken JB, Hoffman B, McKinney AM, Oswood MC. Susceptible vessel sign: identification of arterial occlusion and clinical implications in acute ischaemic stroke. Clin Radiol. 2017;72(2):116–22.

    CAS  PubMed  Google Scholar 

  57. Haacke EM, DelProposto ZS, Chaturvedi S, Sehgal V, Tenzer M, Neelavalli J, Kido D. Imaging cerebral amyloid angiopathy with susceptibility-weighted imaging. AJNR Am J Neuroradiol. 2007;28(2):316–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Dierksen GA, Skehan ME, Khan MA, Jeng J, Nandigam RN, Becker JA, Kumar A, Neal KL, Betensky RA, Frosch MP, Rosand J, Johnson KA, Viswanathan A, Salat DH, Greenberg SM. Spatial relation between microbleeds and amyloid deposits in amyloid angiopathy. Ann Neurol. 2010;68(4):545–8.

    PubMed  PubMed Central  Google Scholar 

  59. Samarasekera N, Smith C, Al-Shahi Salman R. The association between cerebral amyloid angiopathy and intracerebral haemorrhage: systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2012;83(3):275–81.

    PubMed  Google Scholar 

  60. Fisher CM. Hypertensive cerebral hemorrhage. Demonstration of the source of bleeding. J Neuropathol Exp Neurol. 2003;62(1):104–7.

    PubMed  Google Scholar 

  61. Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol. 2010;9(7):689–701.

    PubMed  Google Scholar 

  62. Baig MA, Klein JP, Mechtler LL. Imaging of brain tumors. Continuum (Minneap Minn). 2016;22(5, Neuroimaging):1529–52.

    Google Scholar 

  63. Hsu CC, Watkins TW, Kwan GN, Haacke EM. Susceptibility-weighted imaging of glioma: update on current imaging status and future directions. J Neuroimaging. 2016;26(4):383–90.

    PubMed  Google Scholar 

  64. Li X, Zhu Y, Kang H, Zhang Y, Liang H, Wang S, Zhang W. Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging. 2015;15:4.

    PubMed  PubMed Central  Google Scholar 

  65. Mohammed W, Xunning H, Haibin S, Jingzhi M. Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review. Cancer Imaging. 2013;13:186–95.

    PubMed  PubMed Central  Google Scholar 

  66. Barth M, Nöbauer-Huhmann IM, Reichenbach JR, Mlynárik V, Schöggl A, Matula C, Trattnig S. High-resolution three-dimensional contrast-enhanced blood oxygenation level-dependent magnetic resonance venography of brain tumors at 3 Tesla: first clinical experience and comparison with 1.5 Tesla. Investig Radiol. 2003;38(7):409–14.

    CAS  Google Scholar 

  67. Di Ieva A, Lam T, Alcaide-Leon P, Bharatha A, Montanera W, Cusimano MD. Magnetic resonance susceptibility weighted imaging in neurosurgery: current applications and future perspectives. J Neurosurg. 2015;123(6):1463–75.

    PubMed  Google Scholar 

  68. Li C, Ai B, Li Y, Qi H, Wu L. Susceptibility-weighted imaging in grading brain astrocytomas. Eur J Radiol. 2010;75:e81–5.

    PubMed  Google Scholar 

  69. Wu Z, Mittal S, Kish K, Yu Y, Hu J, Haacke EM. Identification of calcification with MRI using susceptibility-weighted imaging: a case study. J Magn Reson Imaging. 2009;29(1):177–82.

    PubMed  PubMed Central  Google Scholar 

  70. Di Ieva A, Göd S, Grabner G, Grizzi F, Sherif C, Matula C, Tschabitscher M, Trattnig S. Three-dimensional susceptibility-weighted imaging at 7T using fractal-based quantitative analysis to grade gliomas. Neuroradiology. 2013;55:35–40.

    PubMed  Google Scholar 

  71. Saini J, Gupta PK, Sahoo P, Singh A, Patir R, Ahlawat S, Beniwal M, Thennarasu K, Santosh V, Gupta RK. Differentiation of grade II/III and grade IV glioma by combining “T1 contrast-enhanced brain perfusion imaging” and susceptibility-weighted quantitative imaging. Neuroradiology. 2018;60(1):43–50.

    PubMed  Google Scholar 

  72. Park MJ, Kim HS, Jahng GH, Ryu CW, Park SM, Kim SY. Semiquantitative assessment of Intratumoral susceptibility signals using non-contrast-enhanced high-field high-resolution susceptibility-weighted imaging in patients with gliomas: comparison with MR perfusion imaging. AJNR Am J Neuroradiol. 2009;30:1402–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Radbruch A, Wiestler B, Kramp L, Lutz K, Bäumer P, Weiler M, Roethke M, Sahm F, Schlemmer HP, Wick W, Heiland S, Bendszus M. Differentiation of glioblastoma and primary CNS lymphomas using susceptibility weighted imaging. Eur J Radiol. 2013;82(3):552–6.

    PubMed  Google Scholar 

  74. Fahrendorf D, Schwindt W, Wölfer J, Jeibmann A, Kooijman H, Kugel H, Grauer O, Heindel W, Hesselmann V, Bink A. Benefits of contrast-enhanced SWI in patients with glioblastoma multiforme. Eur Radiol. 2013;23(10):2868–79.

    PubMed  Google Scholar 

  75. Toh CH, Wei KC, Chang CN, Hsu PW, Wong HF, Ng SH, Castillo M, Lin CP. Differentiation of pyogenic brain abscesses from necrotic glioblastomas with use of susceptibility-weighted imaging. AJNR Am J Neuroradiol. 2012;33(8):1534–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Radbruch A, Graf M, Kramp L, Wiestler B, Floca R, Bäumer P, Roethke M, Stieltjes B, Schlemmer HP, Heiland S, Bendszus M. Differentiation of brain metastases by percentagewise quantification of intratumoral-susceptibility-signals at 3Tesla. Eur J Radiol. 2012;81(12):4064–8.

    PubMed  Google Scholar 

  77. Liu J, Xia S, Hanks R, Wiseman N, Peng C, Zhou S, Haacke EM, Kou Z. Susceptibility weighted imaging and mapping of micro-hemorrhages and major deep veins after traumatic brain injury. J Neurotrauma. 2016;33(1):10–21.

    PubMed  Google Scholar 

  78. Babikian T, Freier MC, Tong KA, Nickerson JP, Wall CJ, Holshouser BA, Burley T, Riggs ML, Ashwal S. Susceptibility weighted imaging: neuropsychologic outcome and pediatric head injury. Pediatr Neurol. 2005;33(3):184–94.

    PubMed  Google Scholar 

  79. Huang YL, Kuo YS, Tseng YC, Chen DY, Chiu WT, Chen CJ. Susceptibility-weighted MRI in mild traumatic brain injury. Neurology. 2015;84(6):580–5.

    PubMed  Google Scholar 

  80. Kou Z, Wu Z, Tong KA, Holshouser B, Benson RR, Hu J, Haacke EM. The role of advanced MR imaging findings as biomarkers of traumatic brain injury. J Head Trauma Rehabil. 2010;25(4):267–82.

    PubMed  Google Scholar 

  81. Pavlovic D, Pekic S, Stojanovic M, Popovic V. Traumatic brain injury: neuropathological, neurocognitive and neurobehavioral sequelae. Pituitary. 2019;22(3):270–82.

    Google Scholar 

  82. Hunter JV, Wilde EA, Tong KA, Holshouser BA. Emerging imaging tools for use with traumatic brain injury research. J Neurotrauma. 2012;29(4):654–71.

    PubMed  PubMed Central  Google Scholar 

  83. Tong KA, Ashwal S, Holshouser BA, Shutter LA, Herigault G, Haacke EM, Kido DK. Hemorrhagic shearing lesions in children and adolescents with posttraumatic diffuse axonal injury: improved detection and initial results. Radiology. 2003;227:332–9.

    PubMed  Google Scholar 

  84. Tong KA, Ashwal S, Holshouser BA, Nickerson JP, Wall CJ, Shutter LA, Osterdock RJ, Haacke EM, Kido D. Diffuse axonal injury in children: clinical correlation with hemorrhagic lesions. Ann Neurol. 2004;56(1):36–50.

    PubMed  Google Scholar 

  85. Studerus-Germann AM, Gautschi OP, Bontempi P, Thiran JP, Daducci A, Romascano D, von Ow D, Hildebrandt G, von Hessling A, Engel DC. Central nervous system microbleeds in the acute phase are associated with structural integrity by DTI one year after mild traumatic brain injury: a longitudinal study. Neurol Neurochir Pol. 2018;52(6):710–9.

    PubMed  Google Scholar 

  86. Gasparotti R, Pinelli L, Liserre R. New MR sequences in daily practice: susceptibility weighted imaging. A pictorial essay. Insights Imaging. 2011;2(3):335–47.

    PubMed  PubMed Central  Google Scholar 

  87. Lee BC, Vo KD, Kido DK, Mukherjee P, Reichenbach J, Lin W, Yoon MS, Haacke M. MR high-resolution blood oxygenation level-dependent venography of occult (low-flow) vascular lesions. AJNR Am J Neuroradiol. 1999;20(7):1239–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Hu J, Yu Y, Juhasz C, Kou Z, Xuan Y, Latif Z, Kudo K, Chugani HT, Haacke EM. MR susceptibility weighted imaging (SWI) complements conventional contrast enhanced T1 weighted MRI in characterizing brain abnormalities of Sturge-Weber Syndrome. J Magn Reson Imaging. 2008;28(2):300–7.

    PubMed  PubMed Central  Google Scholar 

  89. Mentzel HJ, Dieckmann A, Fitzek C, Brandl U, Reichenbach JR, Kaiser WA. Early diagnosis of cerebral involvement in Sturge-Weber syndrome using high-resolution BOLD MR venography. Pediatr Radiol. 2005;35:85–90.

    PubMed  Google Scholar 

  90. Mooney MA, Zabramski JM. Developmental venous anomalies. Handb Clin Neurol. 2017;143:279–82.

    PubMed  Google Scholar 

  91. Young A, Poretti A, Bosemani T, Goel R, Huisman TAGM. Sensitivity of susceptibility-weighted imaging in detecting developmental venous anomalies and associated cavernomas and microhemorrhages in children. Neuroradiology. 2017;59(8):797–802.

    PubMed  Google Scholar 

  92. Dammann P, Barth M, Zhu Y, Maderwald S, Schlamann M, Ladd ME, Sure U. Susceptibility weighted magnetic resonance imaging of cerebral cavernous malformations: prospects, drawbacks, and first experience at ultra-high field strength (7-Tesla) magnetic resonance imaging. Neurosurg Focus. 2010;29(3):E5.

    PubMed  Google Scholar 

  93. Sparacia G, Speciale C, Banco A, Bencivinni F, Midiri M. Accuracy of SWI sequences compared to T2∗-weighted gradient echo sequences in the detection of cerebral cavernous malformations in the familial form. Neuroradiol J. 2016;29(5):326–35.

    PubMed  PubMed Central  Google Scholar 

  94. Dammann P, Wrede K, Zhu Y, Matsushige T, Maderwald S, Umutlu L, Quick HH, Hehr U, Rath M, Ladd ME, Felbor U, Sure U. Correlation of the venous angioarchitecture of multiple cerebral cavernous malformations with familial or sporadic disease: a susceptibility-weighted imaging study with 7-Tesla MRI. J Neurosurg. 2017;126(2):570–7.

    PubMed  Google Scholar 

  95. Tisell A, Leinhard OD, Warntjes JB, Aalto A, Smedby Ö, Landtblom AM, Lundberg P. Increased concentrations of glutamate and glutamine in normal-appearing white matter of patients with multiple sclerosis and normal MR imaging brain scans. PLoS One. 2013;8(4):e61817.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Laule C, Vavasour IM, Moore GR, Oger J, Li DK, Paty DW, MacKay AL. Water content and myelin water fraction in multiple sclerosis. A T2 relaxation study. J Neurol. 2004;251(3):284–93.

    CAS  PubMed  Google Scholar 

  97. Maggi P, Absinta M, Grammatico M, Vuolo L, Emmi G, Carlucci G, Spagni G, Barilaro A, Repice AM, Emmi L, Prisco D, Martinelli V, Scotti R, Sadeghi N, Perrotta G, Sati P, Dachy B, Reich DS, Filippi M, Massacesi L. Central vein sign differentiates multiple sclerosis from central nervous system inflammatory vasculopathies. Ann Neurol. 2018;83(2):283–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Sati P, Oh J, Constable RT, Evangelou N, Guttmann CR, Henry RG, Klawiter EC, Mainero C, Massacesi L, McFarland H, Nelson F, Ontaneda D, Rauscher A, Rooney WD, Samaraweera AP, Shinohara RT, Sobel RA, Solomon AJ, Treaba CA, Wuerfel J, Zivadinov R, Sicotte NL, Pelletier D, Reich DS, NAIMS Cooperative. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol. 2016;12:714–22.

    PubMed  Google Scholar 

  99. Tan IL, van Schijndel RA, Pouwels PJ, van Walderveen MA, Reichenbach JR, Manoliu RA, Barkhof F. MR venography of multiple sclerosis. AJNR Am J Neuroradiol. 2000;21(6):1039–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Kau T, Taschwer M, Deutschmann H, Schönfelder M, Weber JR, Hausegger KA. The “central vein sign”: is there a place for susceptibility weighted imaging in possible multiple sclerosis? Eur Radiol. 2013;23(7):1956–62.

    PubMed  Google Scholar 

  101. Luo J, Yablonskiy DA, Hildebolt CF, Lancia S, Cross AH. Gradient echo magnetic resonance imaging correlates with clinical measures and allows visualization of veins within multiple sclerosis lesions. Mult Scler. 2014;20(3):349–55.

    PubMed  Google Scholar 

  102. Sparacia G, Agnello F, Gambino A, Sciortino M, Midiri M. Multiple sclerosis: high prevalence of the ‘central vein’ sign in white matter lesions on susceptibility-weighted images. Neuroradiol J. 2018;31(4):356–61.

    PubMed  PubMed Central  Google Scholar 

  103. Maggi P, Mazzoni LN, Moretti M, Grammatico M, Chiti S, Massacesi L. SWI enhances vein detection using gadolinium in multiple sclerosis. Acta Radiol Open. 2015;4(3):2047981614560938.

    PubMed  PubMed Central  Google Scholar 

  104. do Amaral LLF, Fragoso DC, Nunes RH, Littig IA, da Rocha AJ. Gadolinium-enhanced susceptibility-weighted imaging in multiple sclerosis: optimizing the recognition of active plaques for different MR imaging sequences. AJNR Am J Neuroradiol. 2019;40(4):614–9.

    PubMed  PubMed Central  Google Scholar 

  105. Haacke EM, Makki M, Ge Y, Maheshwari M, Sehgal V, Hu J, Selvan M, Wu Z, Latif Z, Xuan Y, Khan O, Garbern J, Grossman RI. Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging. 2009;29(3):537–44.

    PubMed  PubMed Central  Google Scholar 

  106. Habib CA, Liu M, Bawany N, Garbern J, Krumbein I, Mentzel HJ, Reichenbach J, Magnano C, Zivadinov R, Haacke EM. Assessing abnormal iron content in the deep gray matter of patients with multiple sclerosis versus healthy controls. AJNR Am J Neuroradiol. 2012;33(2):252–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Rauscher A, Barth M, Herrmann KH, Witoszynskyj S, Deistung A, Reichenbach JR. Improved elimination of phase effects from background field inhomogeneities for susceptibility weighted imaging at high magnetic field strengths. Magn Reson Imaging. 2008;26(8):1145–51.

    PubMed  Google Scholar 

  108. Chawla S, Kister I, Wuerfel J, Brisset JC, Liu S, Sinnecker T, Dusek P, Haacke EM, Paul F, Ge Y. Iron and non-iron-related characteristics of multiple sclerosis and neuromyelitis optica lesions at 7T MRI. AJNR Am J Neuroradiol. 2016;37(7):1223–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Chawla S, Kister I, Sinnecker T, Wuerfel J, Brisset JC, Paul F, Ge Y. Longitudinal study of multiple sclerosis lesions using ultra-high field (7T) multiparametric MR imaging. PLoS One. 2018;13(9):e0202918.

    PubMed  PubMed Central  Google Scholar 

  110. Dal-Bianco A, Grabner G, Kronnerwetter C, Weber M, Höftberger R, Berger T, Auff E, Leutmezer F, Trattnig S, Lassmann H, Bagnato F, Hametner S. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol. 2017;133(1):25–42.

    CAS  PubMed  Google Scholar 

  111. Hosseini Z, Matusinec J, Rudko DA, Liu J, Kwan BYM, Salehi F, Sharma M, Kremenchutzky M, Menon RS, Drangova M. Morphology-specific discrimination between MS white matter lesions and benign white matter hyperintensities using ultra-high-field MRI. AJNR Am J Neuroradiol. 2018;39(8):1473–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Ndayisaba A, Kaindlstorfer C, Wenning GK. Iron in neurodegeneration—cause or consequence? Front Neurosci. 2019;13:180.

    PubMed  PubMed Central  Google Scholar 

  113. Hare DJ, Raven EP, Roberts BR, Bogeski M, Portbury SD, McLean CA, Masters CL, Connor JR, Bush AI, Crouch PJ, Doble PA. Laser ablation-inductively coupled plasma-mass spectrometry imaging of white and gray matter iron distribution in Alzheimer’s disease frontal cortex. NeuroImage. 2016;137:124–31.

    CAS  PubMed  Google Scholar 

  114. Smith MA, Harris PL, Sayre LM, Perry G. Iron accumulation in Alzheimer disease is a source of redox-generated free radicals. Proc Natl Acad Sci U S A. 1997;94:9866–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Wang D, Zhu D, Wei XE, Li YH, Li WB. Using susceptibility-weighted images to quantify iron deposition differences in amnestic mild cognitive impairment and Alzheimer’s disease. Neurol India. 2013;61(1):26–34.

    PubMed  Google Scholar 

  116. Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol. 2014;13:1045–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Barbosa JH, Santos AC, Tumas V, Liu M, Zheng W, Haacke EM, Salmon CE. Quantifying brain iron deposition in patients with Parkinson’s disease using quantitative susceptibility mapping, R2 and R2. Magn Reson Imaging. 2015;33:559–65.

    CAS  PubMed  Google Scholar 

  118. Griffiths PD, Dobson BR, Jones GR, Clarke DT. Iron in the basal ganglia in Parkinson’s disease: an in vitro study using extended X-ray absorption fine structure and cryo-electron microscopy. Brain. 1999;122:667–73.

    PubMed  Google Scholar 

  119. Liu Z, Shen HC, Lian TH, Mao L, Tang SX, Sun L, Huang XY, Guo P, Cao CJ, Yu SY, Zuo LJ, Wang XM, Chen SD, Chan P, Zhang W. Iron deposition in substantia nigra: abnormal iron metabolism, neuroinflammatory mechanism and clinical relevance. Sci Rep. 2017;7(1):14973.

    PubMed  PubMed Central  Google Scholar 

  120. Bergsland N, Tavazzi E, Laganà MM, Baglio F, Cecconi P, Viotti S, Zivadinov R, Baselli G, Rovaris M. White matter tract injury is associated with deep gray matter iron deposition in multiple sclerosis. J Neuroimaging. 2017;27:107–13.

    PubMed  Google Scholar 

  121. Oshiro S, Morioka MS, Kikuchi M. Dysregulation of iron metabolism in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Adv Pharmacol Sci. 2011;2011:378278.

    PubMed  PubMed Central  Google Scholar 

  122. Prell T, Hartung V, Tietz F, Penzlin S, Ilse B, Schweser F, Deistung A, Bokemeyer M, Reichenbach JR, Witte OW, Grosskreutz J. Susceptibility-weighted imaging provides insight into white matter damage in amyotrophic lateral sclerosis. PLoS One. 2015;10(6):e0131114.

    PubMed  PubMed Central  Google Scholar 

  123. Agrawal S, Fox J, Thyagarajan B, Fox JH. Brain mitochondrial iron accumulates in Huntington’s disease, mediates mitochondrial dysfunction, and can be removed pharmacologically. Free Radic Biol Med. 2018;120:317–29.

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Martelli A, Puccio H. Dysregulation of cellular iron metabolism in Friedreich ataxia: from primary iron-sulfur cluster deficit to mitochondrial iron accumulation. Front Pharmacol. 2014;5:130.

    PubMed  PubMed Central  Google Scholar 

  125. Donatelli G, Ceravolo R, Frosini D, Tosetti M, Bonuccelli U, Cosottini M. Present and future of ultra-high field MRI in neurodegenerative disorders. Curr Neurol Neurosci Rep. 2018;18(6):31.

    PubMed  Google Scholar 

  126. Johns SLM, Ishaque A, Khan M, Yang YH, Wilman AH, Kalra S. Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis. Amyotroph Lateral Scler Frontotemporal Degener. 2019;20(5-6):396–403.

    Google Scholar 

  127. Macerollo A, Perry R, Stamelou M, Batla A, Mazumder AA, Adams ME, Bhatia KP. Susceptibility-weighted imaging changes suggesting brain iron accumulation in Huntington’s disease: an epiphenomenon which causes diagnostic difficulty. Eur J Neurol. 2014;21(2):e16–7.

    CAS  PubMed  Google Scholar 

  128. Park M, Moon Y, Han SH, Moon WJ. Motor cortex hypointensity on susceptibility-weighted imaging: a potential imaging marker of iron accumulation in patients with cognitive impairment. Neuroradiology. 2019;61(6):675–83.

    PubMed  Google Scholar 

  129. Solbach K, Kraff O, Minnerop M, Beck A, Schöls L, Gizewski ER, Ladd ME, Timmann D. Cerebellar pathology in Friedreich’s ataxia: atrophied dentate nuclei with normal iron content. NeuroImage Clin. 2014;6:93–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Schwarz ST, Afzal M, Morgan PS, Bajaj N, Gowland PA, Auer DP. The ‘swallow tail’ appearance of the healthy nigrosome—a new accurate test of Parkinson’s disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS One. 2014;9(4):e93814.

    PubMed  PubMed Central  Google Scholar 

  131. Meijer FJ, Steens SC, van Rumund A, van Cappellen van Walsum AM, Küsters B, Esselink RA, Verbeek MM, Bloem BR, Goraj B. Nigrosome-1 on susceptibility weighted imaging to differentiate Parkinson’s disease from atypical parkinsonism: an in vivo and ex vivo pilot study. Pol J Radiol. 2016;81:363–9.

    PubMed  PubMed Central  Google Scholar 

  132. Gramsch C, Reuter I, Kraff O, Quick HH, Tanislav C, Roessler F, Deuschl C, Forsting M, Schlamann M. Nigrosome 1 visibility at susceptibility weighted 7T MRI - A dependable diagnostic marker for Parkinson’s disease or merely an inconsistent, age-dependent imaging finding? PLoS One. 2017;12(10):e0185489.

    Google Scholar 

  133. Kau T, Hametner S, Endmayr V, Deistung A, Prihoda M, Haimburger E, Menard C, Haider T, Höftberger R, Robinson S, Reichenbach JR, Lassmann H, Traxler H, Trattnig S, Grabner G. Microvessels may confound the “Swallow Tail Sign” in normal aged midbrains: a postmortem 7 T SW-MRI study. J Neuroimaging. 2019;29(1):65–9.

    PubMed  Google Scholar 

  134. Schmidt MA, Engelhorn T, Marxreiter F, Winkler J, Lang S, Kloska S, Goelitz P, Doerfler A. Ultra high-field SWI of the substantia nigra at 7T: reliability and consistency of the swallow-tail sign. BMC Neurol. 2017;17(1):194.

    PubMed  PubMed Central  Google Scholar 

  135. Deistung A, Mentzel HJ, Rauscher A, Witoszynskyj S, Kaiser WA, Reichenbach JR. Demonstration of paramagnetic and diamagnetic cerebral lesions by using susceptibility weighted phase imaging (SWI). Z Med Phys. 2006;16(4):261–7.

    PubMed  Google Scholar 

  136. Azad R, Mittal P, Malhotra A, Gangrade S. Detection and differentiation of focal intracranial calcifications and chronic microbleeds using MRI. J Clin Diagn Res. 2017;11(5):TC19–23.

    PubMed  PubMed Central  Google Scholar 

  137. Ciraci S, Gumus K, Doganay S, Dundar MS, Kaya Ozcora GD, Gorkem SB, Per H, Coskun A. Diagnosis of intracranial calcification and hemorrhage in pediatric patients: comparison of quantitative susceptibility mapping and phase images of susceptibility-weighted imaging. Diagn Interv Imaging. 2017;98(10):707–14.

    CAS  PubMed  Google Scholar 

  138. Gumus K, Koc G, Doganay S, Gorkem SB, Dogan MS, Canpolat M, Coskun A, Bilgen M. Susceptibility-based differentiation of intracranial calcification and hemorrhage in pediatric patients. J Child Neurol. 2015;30(8):1029–36.

    PubMed  Google Scholar 

  139. Zhu WZ, Qi JP, Zhan CJ, Shu HG, Zhang L, Wang CY, Xia LM, Hu JW, Feng DY. Magnetic resonance susceptibility weighted imaging in detecting intracranial calcification and hemorrhage. Chin Med J (Engl). 2008;121(20):2021–5.

    Google Scholar 

  140. Berberat J, Grobholz R, Boxheimer L, Rogers S, Remonda L, Roelcke U. Differentiation between calcification and hemorrhage in brain tumors using susceptibility-weighted imaging: a pilot study. AJR Am J Roentgenol. 2014;202(4):847–50.

    PubMed  Google Scholar 

  141. Zulfiqar M, Dumrongpisutikul N, Intrapiromkul J, Yousem DM. Detection of intratumoral calcification in oligodendrogliomas by susceptibility-weighted MR imaging. AJNR Am J Neuroradiol. 2012;33:858–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Adams LC, Böker SM, Bender YY, Fallenberg EM, Wagner M, Buchert R, Hamm B, Makowski MR. Assessment of intracranial meningioma-associated calcifications using susceptibility-weighted MRI. J Magn Reson Imaging. 2017;46(4):1177–86.

    PubMed  Google Scholar 

  143. Bekiesinska-Figatowska M, Mierzewska H, Jurkiewicz E. Basal ganglia lesions in children and adults. Eur J Radiol. 2013;82(5):837–49.

    PubMed  Google Scholar 

  144. Böttcher J, Sauner D, Jentsch A, Mentzel HJ, Becker H, Reichenbach JR, Kaiser WA. [Visualization of symmetric striopallidodentate calcinosis by using high-resolution susceptibility-weighted MR imaging. An account of the impact of different diagnostic methods of M. Fahr]. Nervenarzt. 2004;75(4):355–61.

    Google Scholar 

  145. Sahin N, Solak A, Genc B, Kulu U. Fahr disease: use of susceptibility-weighted imaging for diagnostic dilemma with magnetic resonance imaging. Quant Imaging Med Surg. 2015;5(4):628–32.

    PubMed  PubMed Central  Google Scholar 

  146. Adams LC, Bressem K, Böker SM, Bender YN, Nörenberg D, Hamm B, Makowski MR. Diagnostic performance of susceptibility-weighted magnetic resonance imaging for the detection of calcifications: a systematic review and meta-analysis. Sci Rep. 2017;7(1):15506.

    PubMed  PubMed Central  Google Scholar 

  147. Neelavalli J, Cheng YC, Jiang J, Haacke EM. Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation. J Magn Reson Imaging. 2009;29(4):937–48.

    PubMed  PubMed Central  Google Scholar 

  148. Kesavadas C, Thomas B, Misra S, Saini J. Attenuation of cerebral veins in susceptibility-weighted MR imaging performed with the patient under general anesthesia. AJNR Am J Neuroradiol. 2008;29:e71.

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Sedlacik J, Löbel U, Kocak M, Loeffler RB, Reichenbach JR, Broniscer A, Patay Z, Hillenbrand CM. Attenuation of cerebral venous contrast in susceptibility-weighted imaging of spontaneously breathing pediatric patients sedated with propofol. AJNR Am J Neuroradiol. 2010;31(5):901–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Fushimi Y, Miki Y, Togashi K, Kikuta K, Hashimoto N, Fukuyama H. A developmental venous anomaly presenting atypical findings on susceptibility-weighted imaging. AJNR Am J Neuroradiol. 2008;29:e56.

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Hsu CC, Haacke EM, Heyn CC, Watkins TW, Krings T. The T1 shine through effect on susceptibility weighted imaging: an under recognized phenomenon. Neuroradiology. 2018;60(3):235–7.

    PubMed  Google Scholar 

  152. Salmela MB, Krishna SH, Martin DJ, Roshan SK, McKinney AM, Tore HG, Knaeble B, Rykken JB, Cayci Z, Jagadeesan BD. All that bleeds is not black: susceptibility weighted imaging of intracranial hemorrhage and the effect of T1 signal. Clin Imaging. 2017;41:69–72.

    PubMed  Google Scholar 

  153. Reichenbach JR, Schweser F, Serres B, Deistung A. Quantitative susceptibility mapping: concepts and applications. Clin Neuroradiol. 2015;25(Suppl 2):225–30.

    PubMed  Google Scholar 

  154. 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. 2009;61(1):196–204.

    PubMed  Google Scholar 

  155. Shmueli K, de Zwart JA, van Gelderen P, Li TQ, Dodd SJ, Duyn JH. Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med. 2009;62(6):1510–22.

    PubMed  PubMed Central  Google Scholar 

  156. Tang J, Liu S, Neelavalli J, Cheng YC, Buch S, Haacke EM. Improving susceptibility mapping using a threshold-based k-space/image domain iterative reconstruction approach. Magn Reson Med. 2013;69(5):1396–407.

    CAS  PubMed  Google Scholar 

  157. Wharton S, Schäfer A, Bowtell R. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med. 2010;63(5):1292–304.

    PubMed  Google Scholar 

  158. Bao L, Li X, Cai C, Chen Z, van Zijl PC. Quantitative susceptibility mapping using structural feature based collaborative reconstruction (SFCR) in the human brain. IEEE Trans Med Imaging. 2016;35(9):2040–50.

    PubMed  PubMed Central  Google Scholar 

  159. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Wu J, Wang Y. Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization: validation and application to brain imaging. Magn Reson Med. 2010;63(1):194–206.

    PubMed  Google Scholar 

  160. Khabipova D, Wiaux Y, Gruetter R, Marques JP. A modulated closed form solution for quantitative susceptibility mapping—a thorough evaluation and comparison to iterative methods based on edge prior knowledge. NeuroImage. 2015;107:163–74.

    PubMed  Google Scholar 

  161. Li W, Wang N, Yu F, Han H, Cao W, Romero R, Tantiwongkosi B, Duong TQ, Liu C. A method for estimating and removing streaking artifacts in quantitative susceptibility mapping. NeuroImage. 2015;108:111–22.

    PubMed  Google Scholar 

  162. Liu T, Liu J, de Rochefort L, Spincemaille P, Khalidov I, Ledoux JR, Wang Y. Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging. Magn Reson Med. 2011;66(3):777–83.

    PubMed  Google Scholar 

  163. Schweser F, Sommer K, Deistung A, Reichenbach JR. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. NeuroImage. 2012;62(3):2083–100.

    PubMed  Google Scholar 

  164. Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed. 2017;30(4):e3569. https://doi.org/10.1002/nbm.3569.

    Article  Google Scholar 

  165. Schweser F, Deistung A, Reichenbach JR. Foundations of MRI phase imaging and processing for quantitative susceptibility mapping (QSM). Z Med Phys. 2016;26(1):6–34.

    PubMed  Google Scholar 

  166. Wang Y, Liu T. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med. 2015;73(1):82–101.

    CAS  PubMed  Google Scholar 

  167. Chen Y, Liu S, Buch S, Hu J, Kang Y, Haacke EM. An interleaved sequence for simultaneous magnetic resonance angiography (MRA), susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM). Magn Reson Imaging. 2018;47:1–6.

    CAS  PubMed  Google Scholar 

  168. Gho SM, Liu C, Li W, Jang U, Kim EY, Hwang D, Kim DH. Susceptibility map-weighted imaging (SMWI) for neuroimaging. Magn Reson Med. 2014;72(2):337–46.

    PubMed  Google Scholar 

  169. Liu S, Mok K, Neelavalli J, Cheng YC, Tang J, Ye Y, Haacke EM. Improved MR venography using quantitative susceptibility-weighted imaging. J Magn Reson Imaging. 2014;40(3):698–708.

    PubMed  Google Scholar 

  170. Bandt SK, de Rochefort L, Chen W, Dimov AV, Spincemaille P, Kopell BH, Gupta A, Wang Y. Clinical integration of quantitative susceptibility mapping magnetic resonance imaging into neurosurgical practice. World Neurosurg. 2019;122:e10–1.

    PubMed  Google Scholar 

  171. Eskreis-Winkler S, Zhang Y, Zhang J, Liu Z, Dimov A, Gupta A, Wang Y. The clinical utility of QSM: disease diagnosis, medical management, and surgical planning. NMR Biomed. 2017;30(4):e3668. https://doi.org/10.1002/nbm.3668.

    Article  CAS  Google Scholar 

  172. Zhang S, Liu Z, Nguyen TD, Yao Y, Gillen KM, Spincemaille P, Kovanlikaya I, Gupta A, Wang Y. Clinical feasibility of brain quantitative susceptibility mapping. Magn Reson Imaging. 2019;60:44–51.

    PubMed  PubMed Central  Google Scholar 

  173. Bollmann S, Kristensen MH, Larsen MS, Olsen MV, Pedersen MJ, Østergaard LR, O’Brien K, Langkammer C, Fazlollahi A, Barth M. SHARQnet—sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network. Z Med Phys. 2019;29(2):139–49.

    PubMed  Google Scholar 

  174. Bollmann S, Rasmussen KGB, Kristensen M, Blendal RG, Østergaard LR, 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;195:373–83.

    PubMed  Google Scholar 

  175. Yoon J, Gong E, Chatnuntawech I, Bilgic B, Lee J, Jung W, Ko J, Jung H, Setsompop K, Zaharchuk G, Kim EY, Pauly J, Lee J. Quantitative susceptibility mapping using deep neural network: QSMnet. NeuroImage. 2018;179:199–206.

    PubMed  Google Scholar 

  176. Liu S, Utriainen D, Chai C, Chen Y, Wang L, Sethi SK, Xia S, Haacke EM. Cerebral microbleed detection using susceptibility weighted imaging and deep learning. NeuroImage. 2019;198:271–82.

    PubMed  Google Scholar 

  177. Zhang X, Zhang Y, Hu Q. Deep learning based vein segmentation from susceptibility-weighted images. Computing. 2019;101:637–52.

    Google Scholar 

  178. Bilgic B, Gagoski BA, Cauley SF, Fan AP, Polimeni JR, Grant PE, Wald LL, Setsompop K. Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med. 2015;73(6):2152–62.

    PubMed  Google Scholar 

  179. Bilgic B, Ye H, Wald LL, Setsompop K. Simultaneous Time Interleaved MultiSlice (STIMS) for rapid susceptibility weighted acquisition. NeuroImage. 2017;155:577–86.

    PubMed  Google Scholar 

  180. Conklin J, Longo MG, Cauley S, Setsompop K, Kirsch J, Liu W, Ahn S, Beck T, Gonzalez R, Schaefer P, Rapalino O, Huang S. Prospective evaluation of wave-CAIPI susceptibility-weighted imaging (SWI) compared to conventional 3D SWI in a clinical setting. Proc Int Soc Mag Reson Med. 2019;27:3092.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jürgen R. Reichenbach .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Reichenbach, J.R. (2020). Susceptibility Weighted Imaging. In: Mannil, M., Winklhofer, SX. (eds) Neuroimaging Techniques in Clinical Practice. Springer, Cham. https://doi.org/10.1007/978-3-030-48419-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-48419-4_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48418-7

  • Online ISBN: 978-3-030-48419-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics