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Susceptibility Weighted MRI in Rodents at 9.4 T

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Preclinical MRI

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1718))

Abstract

Susceptibility Weighted Imaging (SWI) is an established part of the clinical neuroimaging toolbox and, since its inception, has also successfully been used in various preclinical studies. Exploiting the effect of variations of magnetic susceptibility between different tissues on the externally applied, static, homogeneous magnetic field, the method visualizes venous vasculature, hemorrhages and blood degradation products, calcifications, and tissue iron deposits. The chapter describes in vivo and ex vivo protocols for preclinical SWI in rodents.

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References

  1. Haacke EM, Xu Y, Cheng YC, Reichenbach JR (2004) Susceptibility weighted imaging (SWI). Magn Reson Med 52(3):612–618. https://doi.org/10.1002/mrm.20198

    Article  PubMed  Google Scholar 

  2. Reichenbach JR, Barth M, Haacke EM, Klarhofer M, Kaiser WA, Moser E (2000) High-resolution MR venography at 3.0 Tesla. J Comput Assist Tomogr 24(6):949–957

    Article  CAS  PubMed  Google Scholar 

  3. Reichenbach JR, Haacke EM (2001) High-resolution BOLD venographic imaging: a window into brain function. NMR Biomed 14(7–8):453–467

    Article  CAS  PubMed  Google Scholar 

  4. Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM (1997) Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 204(1):272–277. https://doi.org/10.1148/radiology.204.1.9205259

    Article  CAS  PubMed  Google Scholar 

  5. Ahmed BA, Kumar NA, Vijayan D, Magudeeswaran PK, Ganesan P (2016) Magnetic resonance imaging susceptibility- weighted imaging is more reliable to detect hemorrhage and calcification than magnetic resonance imaging T2*-weighted gradient echo in brain imaging. Int J Sci Study 4(3):203–205. 10.17354/ijss/2016/352

    Google Scholar 

  6. Barbosa JH, Santos AC, Salmon CE (2015) Susceptibility weighted imaging: differentiating between calcification and hemosiderin. Radiol Bras 48(2):93–100. https://doi.org/10.1590/0100-3984.2014.0010

    Article  PubMed  PubMed Central  Google Scholar 

  7. Berberat J, Grobholz R, Boxheimer L, Rogers S, Remonda L, Roelcke U (2014) Differentiation between calcification and hemorrhage in brain tumors using susceptibility-weighted imaging: a pilot study. AJR Am J Roentgenol 202(4):847–850. https://doi.org/10.2214/AJR.13.10745

    Article  PubMed  Google Scholar 

  8. Deistung A, Mentzel HJ, Rauscher A, Witoszynskyj S, Kaiser WA, Reichenbach JR (2006) Demonstration of paramagnetic and diamagnetic cerebral lesions by using susceptibility weighted phase imaging (SWI). Z Med Phys 16(4):261–267

    Article  PubMed  Google Scholar 

  9. Li C, Imbesi S, Lee R, Haacke E, Chen J (2016) Potential pitfalls when differentiating hemorrhage and calcium on susceptibility-weighted images. Neurographics 6(3):123–126

    Article  Google Scholar 

  10. Ong BC, Stuckey SL (2010) Susceptibility weighted imaging: a pictorial review. J Med Imaging Radiat Oncol 54(5):435–449. https://doi.org/10.1111/j.1754-9485.2010.02208.x

    Article  PubMed  Google Scholar 

  11. Chen X, Zeng C, Luo T, Ouyang Y, Lv F, Rumzan R, Wang Z, Li Q, Wang J, Hou H, Huang F, Li Y (2012) Iron deposition of the deep grey matter in patients with multiple sclerosis and neuromyelitis optica: a control quantitative study by 3D-enhanced susceptibility-weighted angiography (ESWAN). Eur J Radiol 81(4):e633–e639. https://doi.org/10.1016/j.ejrad.2012.01.003

    Article  PubMed  Google Scholar 

  12. Du S, Sah SK, Zeng C, Wang J, Liu Y, Xiong H, Li Y (2015) Iron deposition in the gray matter in patients with relapse-remitting multiple sclerosis: a longitudinal study using three-dimensional (3D)-enhanced T2*-weighted angiography (ESWAN). Eur J Radiol 84(7):1325–1332. https://doi.org/10.1016/j.ejrad.2015.04.013

    Article  PubMed  Google Scholar 

  13. Hagemeier J, Weinstock-Guttman B, Bergsland N, Heininen-Brown M, Carl E, Kennedy C, Magnano C, Hojnacki D, Dwyer MG, Zivadinov R (2012) Iron deposition on SWI-filtered phase in the subcortical deep gray matter of patients with clinically isolated syndrome may precede structure-specific atrophy. AJNR Am J Neuroradiol 33(8):1596–1601. https://doi.org/10.3174/ajnr.A3030

    Article  CAS  PubMed  Google Scholar 

  14. Ning N, Zhang L, Gao J, Zhang Y, Ren Z, Niu G, Dai Y, EX W, Guo Y, Yang J (2014) Assessment of iron deposition and white matter maturation in infant brains by using enhanced T2 star weighted angiography (ESWAN): R2* versus phase values. PLoS One 9(2):e89888. https://doi.org/10.1371/journal.pone.0089888

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang D, Li WB, Wei XE, Li YH, Dai YM (2012) An investigation of age-related iron deposition using susceptibility weighted imaging. PLoS One 7(11):e50706. https://doi.org/10.1371/journal.pone.0050706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang D, Zhu D, Wei XE, Li YH, Li WB (2013) Using susceptibility-weighted images to quantify iron deposition differences in amnestic mild cognitive impairment and Alzheimer’s disease. Neurol India 61(1):26–34. https://doi.org/10.4103/0028-3886.107924

    Article  PubMed  Google Scholar 

  17. Xu X, Wang Q, Zhang M (2008) Age, gender, and hemispheric differences in iron deposition in the human brain: an in vivo MRI study. NeuroImage 40(1):35–42. https://doi.org/10.1016/j.neuroimage.2007.11.017

    Article  CAS  PubMed  Google Scholar 

  18. Yu J, Qi F, Wang N, Gao P, Dai S, Lu Y, Su Q, Du Y, Che F (2014) Increased iron level in motor cortex of amyotrophic lateral sclerosis patients: an in vivo MR study. Amyotroph Lateral Scler Frontotemporal Degener 15(5–6):357–361. https://doi.org/10.3109/21678421.2014.906618

    Article  CAS  PubMed  Google Scholar 

  19. Ogawa S, Lee TM, Nayak AS, Glynn P (1990) Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med 14(1):68–78

    Article  CAS  PubMed  Google Scholar 

  20. Krishnan AS, Lansley JA, Jager HR, Mankad K (2015) New vistas in clinical practice: susceptibility-weighted imaging. Quant Imaging Med Surg 5(3):448–452. https://doi.org/10.3978/j.issn.2223-4292.2015.03.03

    PubMed  PubMed Central  Google Scholar 

  21. 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 (2017) Susceptibility-weighted imaging: current status and future directions. NMR Biomed 30(4):e3552. https://doi.org/10.1002/nbm.3552

    Article  Google Scholar 

  22. Bir C, Vandevord P, Shen Y, Raza W, Haacke EM (2012) Effects of variable blast pressures on blood flow and oxygen saturation in rat brain as evidenced using MRI. Magn Reson Imaging 30(4):527–534. https://doi.org/10.1016/j.mri.2011.12.003

    Article  PubMed  Google Scholar 

  23. Blasiak B, Landry J, Tyson R, Sharp J, Iqbal U, Abulrob A, Rushforth D, Matyas J, Ponjevic D, Sutherland GR, Wolfsberger S, Tomanek B (2014) Molecular susceptibility weighted imaging of the glioma rim in a mouse model. J Neurosci Methods 226:132–138. https://doi.org/10.1016/j.jneumeth.2014.01.034

    Article  PubMed  Google Scholar 

  24. Nathoo N, Agrawal S, Wu Y, Haylock-Jacobs S, Yong VW, Foniok T, Barnes S, Obenaus A, Dunn JF (2013) Susceptibility-weighted imaging in the experimental autoimmune encephalomyelitis model of multiple sclerosis indicates elevated deoxyhemoglobin, iron deposition and demyelination. Mult Scler 19(6):721–731. https://doi.org/10.1177/1352458512460602

    Article  PubMed  Google Scholar 

  25. Nathoo N, Rogers JA, Yong VW, Dunn JF (2015) Detecting deoxyhemoglobin in spinal cord vasculature of the experimental autoimmune encephalomyelitis mouse model of multiple sclerosis using susceptibility MRI and hyperoxygenation. PLoS One 10(5):e0127033. https://doi.org/10.1371/journal.pone.0127033

    Article  PubMed  PubMed Central  Google Scholar 

  26. Shen Y, Kou Z, Kreipke CW, Petrov T, Hu J, Haacke EM (2007) In vivo measurement of tissue damage, oxygen saturation changes and blood flow changes after experimental traumatic brain injury in rats using susceptibility weighted imaging. Magn Reson Imaging 25(2):219–227. https://doi.org/10.1016/j.mri.2006.09.018

    Article  PubMed  Google Scholar 

  27. Verma SK, Kan EM, Lu J, Ng KC, Ling EA, Seramani S, Kn BP, Wong YC, Tan MH, Velan SS (2015) Multi-echo susceptibility-weighted imaging and histology of open-field blast-induced traumatic brain injury in a rat model. NMR Biomed 28(9):1069–1077. https://doi.org/10.1002/nbm.3351

    Article  CAS  PubMed  Google Scholar 

  28. Yang SH, Lin J, Lu F, Dai YY, Han ZH, CX F, FL H, HC G (2016) Contrast-enhanced susceptibility weighted imaging with ultrasmall superparamagnetic iron oxide improves the detection of tumor vascularity in a hepatocellular carcinoma nude mouse model. J Magn Reson Imaging 44(2):288–295. https://doi.org/10.1002/jmri.25167

    Article  PubMed  Google Scholar 

  29. Kim SG, Park SH (2011) High-resolution venographic BOLD MRI of animal brain at 9.4 T: implications for BOLD fMRI. Susceptibility weighted imaging in MRI: basic concepts and clinical applications. Wiley-Blackwell, Hoboken, pp 637–647

    Google Scholar 

  30. Ogawa S, Lee TM (1990) Magnetic resonance imaging of blood vessels at high fields: in vivo and in vitro measurements and image simulation. Magn Reson Med 16(1):9–18

    Article  CAS  PubMed  Google Scholar 

  31. Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87(24):9868–9872

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Park SH, Masamoto K, Hendrich K, Kanno I, Kim SG (2008) Imaging brain vasculature with BOLD microscopy: MR detection limits determined by in vivo two-photon microscopy. Magn Reson Med 59(4):855–865. https://doi.org/10.1002/mrm.21573

    Article  PubMed  PubMed Central  Google Scholar 

  33. Harrison PM, Arosio P (1996) The ferritins: molecular properties, iron storage function and cellular regulation. Biochim Biophys Acta 1275(3):161–203

    Article  PubMed  Google Scholar 

  34. Buch S, Liu S, Ye Y, Cheng YC, Neelavalli J, Haacke EM (2015) Susceptibility mapping of air, bone, and calcium in the head. Magn Reson Med 73(6):2185–2194. https://doi.org/10.1002/mrm.25350

    Article  CAS  PubMed  Google Scholar 

  35. Chen W, Zhu W, Kovanlikaya I, Kovanlikaya A, Liu T, Wang S, Salustri C, Wang Y (2014) Intracranial calcifications and hemorrhages: characterization with quantitative susceptibility mapping. Radiology 270(2):496–505. https://doi.org/10.1148/radiol.13122640

    Article  PubMed  PubMed Central  Google Scholar 

  36. Deistung A, Schweser F, Wiestler B, Abello M, Roethke M, Sahm F, Wick W, Nagel AM, Heiland S, Schlemmer HP, Bendszus M, Reichenbach JR, Radbruch A (2013) Quantitative susceptibility mapping differentiates between blood depositions and calcifications in patients with glioblastoma. PLoS One 8(3):e57924. https://doi.org/10.1371/journal.pone.0057924

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schweser F, Deistung A, Lehr BW, Reichenbach JR (2010) Differentiation between diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med Phys 37(10):5165–5178. https://doi.org/10.1118/1.3481505

    Article  PubMed  Google Scholar 

  38. Straub S, Laun FB, Emmerich J, Jobke B, Hauswald H, Katayama S, Herfarth K, Schlemmer HP, Ladd ME, Ziener CH, Bonekamp D, Rothke MC (2017) Potential of quantitative susceptibility mapping for detection of prostatic calcifications. J Magn Reson Imaging 45(3):889–898. https://doi.org/10.1002/jmri.25385

    Article  PubMed  Google Scholar 

  39. Rauscher A, Sedlacik J, Deistung A, Mentzel HJ, Reichenbach JR (2006) Susceptibility weighted imaging: data acquisition, image reconstruction and clinical applications. Z Med Phys 16(4):240–250

    Article  PubMed  Google Scholar 

  40. Deistung A, Rauscher A, Sedlacik J, Stadler J, Witoszynskyj S, Reichenbach JR (2008) Susceptibility weighted imaging at ultra high magnetic field strengths: theoretical considerations and experimental results. Magn Reson Med 60(5):1155–1168. https://doi.org/10.1002/mrm.21754

    Article  PubMed  Google Scholar 

  41. Duyn JH, van Gelderen P, Li TQ, de Zwart JA, Koretsky AP, Fukunaga M (2007) High-field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci U S A 104(28):11796–11801. https://doi.org/10.1073/pnas.0610821104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hagemeier J, Dwyer MG, Bergsland N, Schweser F, Magnano CR, Heininen-Brown M, Ramasamy DP, Carl E, Kennedy C, Melia R, Polak P, Deistung A, Geurts JJ, Reichenbach JR, Zivadinov R (2013) Effect of age on MRI phase behavior in the subcortical deep gray matter of healthy individuals. AJNR Am J Neuroradiol 34(11):2144–2151. https://doi.org/10.3174/ajnr.A3569

    Article  CAS  PubMed  Google Scholar 

  43. Rumzan R, Wang JJ, Zeng C, Chen X, Li Y, Luo T, Lv F, Wang ZP, Hou H, Huang F (2013) Iron deposition in the precentral grey matter in patients with multiple sclerosis: a quantitative study using susceptibility-weighted imaging. Eur J Radiol 82(2):e95–e99. https://doi.org/10.1016/j.ejrad.2012.09.006

    Article  PubMed  Google Scholar 

  44. Wang D, Li YY, Luo JH, Li YH (2014) Age-related iron deposition in the basal ganglia of controls and Alzheimer disease patients quantified using susceptibility weighted imaging. Arch Gerontol Geriatr 59(2):439–449. https://doi.org/10.1016/j.archger.2014.04.002

    Article  CAS  PubMed  Google Scholar 

  45. Hagemeier J, Weinstock-Guttman B, Heininen-Brown M, Poloni GU, Bergsland N, Schirda C, Magnano CR, Kennedy C, Carl E, Dwyer MG, Minagar A, Zivadinov R (2013) Gray matter SWI-filtered phase and atrophy are linked to disability in MS. Front Biosci (Elite Ed) 5:525–532

    Article  Google Scholar 

  46. Peters AM, Brookes MJ, Hoogenraad FG, Gowland PA, Francis ST, Morris PG, Bowtell R (2007) T2* measurements in human brain at 1.5, 3 and 7 T. Magn Reson Imaging 25(6):748–753. https://doi.org/10.1016/j.mri.2007.02.014

    Article  PubMed  Google Scholar 

  47. Bourekas EC, Christoforidis GA, Abduljalil AM, Kangarlu A, Chakeres DW, Spigos DG, Robitaille PM (1999) High resolution MRI of the deep gray nuclei at 8 Tesla. J Comput Assist Tomogr 23(6):867–874

    Article  CAS  PubMed  Google Scholar 

  48. Roemer PB, Edelstein WA, Hayes CE, Souza SP, Mueller OM (1990) The NMR phased array. Magn Reson Med 16(2):192–225

    Article  CAS  PubMed  Google Scholar 

  49. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320. https://doi.org/10.1109/TMI.2010.2046908

    Article  PubMed  PubMed Central  Google Scholar 

  50. Frank LR, Crawley AP, Buxton RB (1992) Elimination of oblique flow artifacts in magnetic resonance imaging. Magn Reson Med 25(2):299–307

    Article  CAS  PubMed  Google Scholar 

  51. Denk C, Rauscher A (2010) Susceptibility weighted imaging with multiple echoes. J Magn Reson Imaging 31(1):185–191. https://doi.org/10.1002/jmri.21995

    Article  PubMed  Google Scholar 

  52. Wu B, Li W, Avram AV, Gho SM, Liu C (2012) Fast and tissue-optimized mapping of magnetic susceptibility and T2* with multi-echo and multi-shot spirals. NeuroImage 59(1):297–305. https://doi.org/10.1016/j.neuroimage.2011.07.019

    Article  PubMed  Google Scholar 

  53. Duyn J (2013) MR susceptibility imaging. J Magn Reson 229:198–207. https://doi.org/10.1016/j.jmr.2012.11.013

    Article  CAS  PubMed  Google Scholar 

  54. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y (2015) Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 33(1):1–25. https://doi.org/10.1016/j.mri.2014.09.004

    Article  PubMed  Google Scholar 

  55. Liu C, Li W, Tong KA, Yeom KW, Kuzminski S (2015) Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 42(1):23–41. https://doi.org/10.1002/jmri.24768

    Article  PubMed  Google Scholar 

  56. Liu C, Wei H, Gong NJ, Cronin M, Dibb R, Decker K (2015) Quantitative susceptibility mapping: contrast mechanisms and clinical applications. Tomography 1(1):3–17. 10.18383/j.tom.2015.00136

    Article  PubMed  PubMed Central  Google Scholar 

  57. Reichenbach JR, Schweser F, Serres B, Deistung A (2015) Quantitative susceptibility mapping: concepts and applications. Clin Neuroradiol 25(Suppl 2):225–230. https://doi.org/10.1007/s00062-015-0432-9

    Article  PubMed  Google Scholar 

  58. Wang Y, Liu T (2015) Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 73(1):82–101. https://doi.org/10.1002/mrm.25358

    Article  CAS  PubMed  Google Scholar 

  59. Xu B, Liu T, Spincemaille P, Prince M, Wang Y (2014) Flow compensated quantitative susceptibility mapping for venous oxygenation imaging. Magn Reson Med 72(2):438–445. https://doi.org/10.1002/mrm.24937

    Article  PubMed  Google Scholar 

  60. Shen Y, Kou Z, Haacke EM (2011) Susceptibility weighted imaging in rodents. Susceptibility weighted imaging in MRI: basic concepts and clinical applications. Wiley-Blackwell, Hoboken, pp 649–667

    Book  Google Scholar 

  61. Kim S, Pickup S, Hsu O, Poptani H (2009) Enhanced delineation of white matter structures of the fixed mouse brain using Gd-DTPA in microscopic MRI. NMR Biomed 22(3):303–309. https://doi.org/10.1002/nbm.1324

    Article  PubMed  Google Scholar 

  62. Aggarwal M, Mori S, Shimogori T, Blackshaw S, Zhang J (2010) Three-dimensional diffusion tensor microimaging for anatomical characterization of the mouse brain. Magn Reson Med 64(1):249–261. https://doi.org/10.1002/mrm.22426

    Article  PubMed  PubMed Central  Google Scholar 

  63. Andrews TJ, Osborne MT, Does MD (2006) Diffusion of myelin water. Magn Reson Med 56(2):381–385. https://doi.org/10.1002/mrm.20945

    Article  PubMed  Google Scholar 

  64. Shmueli K, van Gelderen P, Li T, Duyn J (2008) High resolution human brain susceptibility maps calculated from 7 Tesla MRI phase data. 16th Annual Meeting of the International Society for Magnetic Resonance in Medicine, p 642

    Google Scholar 

  65. van Duijn S, Nabuurs RJ, van Rooden S, Maat-Schieman ML, van Duinen SG, van Buchem MA, van der Weerd L, Natte R (2011) MRI artifacts in human brain tissue after prolonged formalin storage. Magn Reson Med 65(6):1750–1758. https://doi.org/10.1002/mrm.22758

    Article  PubMed  Google Scholar 

  66. Hakkarainen H, Sierra A, Mangia S, Garwood M, Michaeli S, Grohn O, Liimatainen T (2016) MRI relaxation in the presence of fictitious fields correlates with myelin content in normal rat brain. Magn Reson Med 75(1):161–168. https://doi.org/10.1002/mrm.25590

    Article  PubMed  Google Scholar 

  67. Meijer FJ, Goraj B, Bloem BR, Esselink RA (2017) How I do it: clinical application of brain MRI in the diagnostic work-up of Parkinsonism. J Parkinsons Dis 7:211. https://doi.org/10.3233/JPD-150733

    Article  PubMed  PubMed Central  Google Scholar 

  68. Birkl C, Langkammer C, Krenn H, Goessler W, Ernst C, Haybaeck J, Stollberger R, Fazekas F, Ropele S (2015) Iron mapping using the temperature dependency of the magnetic susceptibility. Magn Reson Med 73(3):1282–1288. https://doi.org/10.1002/mrm.25236

    Article  CAS  PubMed  Google Scholar 

  69. Dal-Bianco A, Grabner G, Kronnerwetter C, Weber M, Hoftberger R, Berger T, Auff E, Leutmezer F, Trattnig S, Lassmann H, Bagnato F, Hametner S (2017) Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol 133(1):25–42. https://doi.org/10.1007/s00401-016-1636-z

    Article  CAS  PubMed  Google Scholar 

  70. Schweser F, Deistung A, Reichenbach JR (2016) Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM). Z Med Phys 26(1):6–34. https://doi.org/10.1016/j.zemedi.2015.10.002

    Article  PubMed  Google Scholar 

  71. Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34(6):910–914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Robinson SD, Bredies K, Khabipova D, Dymerska B, Marques JP, Schweser F (2017) An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 30(4):e3601. https://doi.org/10.1002/nbm.3601

    Article  Google Scholar 

  73. Schweser F, Robinson SD, de Rochefort L, Li W, Bredies K (2017) An illustrated comparison of processing methods for phase MRI and QSM: removal of background field contributions from sources outside the region of interest. NMR Biomed 30(4):e3604. https://doi.org/10.1002/nbm.3604

    Article  Google Scholar 

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Acknowledgements

We are grateful to Drs. David Poulsen (Department of Neurosurgery, University at Buffalo) and Claire Modica (Buffalo Neuroimaging Analysis Center, Department of Neurology, University at Buffalo) for support with the ex vivo experiments. Research reported in this publication was funded by the National Center for Advancing Translational Sciences of the National Institutes of Health under award Number UL1TR001412. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Ferdinand Schweser, Marilena Preda & Robert Zivadinov

  2. Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Ferdinand Schweser, Marilena Preda & Robert Zivadinov

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  1. Ferdinand Schweser
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  2. Marilena Preda
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  3. Robert Zivadinov
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Correspondence to Ferdinand Schweser .

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  1. BIONAND, Andalusian Centre for Nanomedicine and Biotechnology, Junta de Andalucía, Universidad de Málaga, Málaga, Spain

    María Luisa García Martín

  2. Instituto de Investigaciones Biomédicas “Alberto Sols”, CSIC/UAM, Madrid, Spain

    Pilar López Larrubia

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Schweser, F., Preda, M., Zivadinov, R. (2018). Susceptibility Weighted MRI in Rodents at 9.4 T. In: García Martín, M., López Larrubia, P. (eds) Preclinical MRI. Methods in Molecular Biology, vol 1718. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7531-0_13

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