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High Angular Resolution Diffusion Imaging

  • Chapter
Diffusion Tensor Imaging

Abstract

The development of diffusion tensor imaging (DTI) over two decades ago was a pivotal moment in the field of diffusion Magnetic Resonance Imaging (MRI), offering for the first time a glimpse into the connectivity of brain white matter in a completely noninvasive manner. However, although DTI is still the most commonly used model to relate the diffusion-weighted MRI signal to the underlying diffusion process, it is now widely acknowledged to be inadequate for this purpose. The limitations of the DTI model have important implications for the interpretation of the various quantitative parameters derived from it; for instance, it is now clear that anisotropy cannot simply be regarded as a surrogate marker of white matter integrity. These limitations are even more profound for applications such as diffusion-based tractography. In this chapter we discuss the practical limitations of the DTI model and review recent developments in the field, many of which are based on the high angular resolution diffusion-weighted imaging (HARDI) data acquisition strategy.

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Notes

  1. 1.

    This is discussed in more detail in the “constant anisotropy” assumption section below.

  2. 2.

    Although there are assumptions on the way data are acquired, as discussed in the section entitled The narrow pulse approximation below.

  3. 3.

    The q-value is a measure of the strength of the diffusion weighting, akin to the b-value. It is given by q = (γ/2π)δG, where δ and G are the DW gradient pulse duration and amplitude respectively, and γ is the magnetogyric ratio.

References

  1. Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL. Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med. 2001;45(5):770–80.

    Article  CAS  PubMed  Google Scholar 

  2. Alexander DC, Barker GJ, Arridge SR. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn Reson Med. 2002;48(2):331–40.

    Article  CAS  PubMed  Google Scholar 

  3. Frank LR. Anisotropy in high angular resolution diffusion-weighted MRI. Magn Reson Med. 2001;45(6):935–9.

    Article  CAS  PubMed  Google Scholar 

  4. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med. 2002;48(4):577–82.

    Article  PubMed  Google Scholar 

  5. Seunarine KK, Alexander DC. Multiple fibres: beyond the diffusion tensor. In: Johansen-Berg H, Behrens TEJ, editors. Diffusion MRI: from quantitative measurement to in-vivo neuroanatomy. San Diego, CA: Elsevier; 2009. p. 56–74.

    Google Scholar 

  6. Tournier JD. The biophysics of crossing fibres. In: Jones DK, editor. Diffusion MRI: theory, methods, and applications. New York, NY: Oxford University Press; 2010. p. 465–82.

    Chapter  Google Scholar 

  7. Tournier J-D, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med. 2011;65(6):1532–56.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Alexander DC. Multiple-fiber reconstruction algorithms for diffusion MRI. Ann N Y Acad Sci. 2005;1064(1):113–33.

    Article  PubMed  Google Scholar 

  9. Alexander DC, Barker GJ. Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. Neuroimage. 2005;27(2):357–67.

    Article  PubMed  Google Scholar 

  10. Tournier J-D, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004;23(3):1176–85.

    Article  PubMed  Google Scholar 

  11. Vos SB, Jones DK, Viergever MA, Leemans A. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage. 2011;55(4):1566–76.

    Article  PubMed  Google Scholar 

  12. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111(3):209–19.

    Article  CAS  PubMed  Google Scholar 

  13. Jeurissen B, Leemans A, Tournier J-D, Jones DK, Sijbers J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human Brain Mapping [Internet]. 2012 May 19 [cited 2012 Jun 15]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/22611035

  14. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage. 2007;34(1):144–55.

    Article  CAS  PubMed  Google Scholar 

  15. Pierpaoli C, Barnett A, Pajevic S, Chen R, Penix L, Virta A, et al. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage. 2001;13(6):1174–85.

    Article  CAS  PubMed  Google Scholar 

  16. Berman J. Diffusion MR, tractography as a tool for surgical planning. Magn Reson Imaging Clin N Am. 2009;17(2):205–14.

    Article  PubMed  Google Scholar 

  17. Farquharson S, Tournier J-D, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg. 2013;118:1367.

    Article  PubMed  Google Scholar 

  18. Fernandez-Miranda JC, Pathak S, Engh J, Jarbo K, Verstynen T, Yeh F-C, et al. High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications. Neurosurgery. 2012;71(2):430–53.

    Article  PubMed  Google Scholar 

  19. Kinoshita M, Yamada K, Hashimoto N, Kato A, Izumoto S, Baba T, et al. Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage. 2005;25(2):424–9.

    Article  PubMed  Google Scholar 

  20. Nimsky C, Ganslandt O, Fahlbusch R. Implementation of fiber tract navigation. Neurosurgery. 2006;58(4 Suppl 2):ONS–292–303. discussion ONS–303–304.

    Google Scholar 

  21. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 2010;23(7):803–20.

    Article  PubMed  Google Scholar 

  22. Vos SB, Jones DK, Jeurissen B, Viergever MA, Leemans A. The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. Neuroimage. 2012;59(3):2208–16.

    Article  PubMed  Google Scholar 

  23. Wheeler-Kingshott CAM, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med. 2009;61(5):1255–60.

    Article  PubMed  Google Scholar 

  24. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36(6):893–906.

    Article  CAS  PubMed  Google Scholar 

  25. Douaud G, Jbabdi S, Behrens TEJ, Menke RA, Gass A, Monsch AU, et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage. 2011;55(3):880–90.

    Article  PubMed  Google Scholar 

  26. Jones DK, Knösche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage. 2013;73:239–54.

    Article  PubMed  Google Scholar 

  27. Fernandez-Miranda JC. Editorial: beyond diffusion tensor imaging. J Neurosurg. 2013;118(6):1363–6.

    Article  PubMed  Google Scholar 

  28. Jones DK. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn Reson Med. 2004;51(4):807–15.

    Article  PubMed  Google Scholar 

  29. Tournier J-D, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage. 2007;35(4):1459–72.

    Article  PubMed  Google Scholar 

  30. Parker GJM, Alexander DC. Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information. Inf Process Med Imaging. 2003;18:684–95.

    Article  PubMed  Google Scholar 

  31. Hosey T, Williams G, Ansorge R. Inference of multiple fiber orientations in high angular resolution diffusion imaging. Magn Reson Med. 2005;54(6):1480–9.

    Article  PubMed  Google Scholar 

  32. Hosey TP, Harding SG, Carpenter TA, Ansorge RE, Williams GB. Application of a probabilistic double-fibre structure model to diffusion-weighted MR images of the human brain. Magn Reson Imaging. 2008;26(2):236–45.

    Article  PubMed  Google Scholar 

  33. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27(1):48–58.

    Article  PubMed  Google Scholar 

  34. Assaf Y, Freidlin RZ, Rohde GK, Basser PJ. New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med. 2004;52(5):965–78.

    Article  PubMed  Google Scholar 

  35. Tournier J, Calamante F, Connelly A. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol. 2012;22(1):53–66.

    Article  Google Scholar 

  36. Anderson AW. Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn Reson Med. 2005;54(5):1194–206.

    Article  PubMed  Google Scholar 

  37. Dell’Acqua F, Rizzo G, Scifo P, Clarke RA, Scotti G, Fazio F. A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging. IEEE Trans Biomed Eng. 2007;54(3):462–72.

    Article  PubMed  Google Scholar 

  38. Kaden E, Knösche TR, Anwander A. Parametric spherical deconvolution: inferring anatomical connectivity using diffusion MR imaging. Neuroimage. 2007;37(2):474–88.

    Article  PubMed  Google Scholar 

  39. Seunarine KK, Alexander DC. Linear Persistent Angular Structure MRI and non-linear Spherical Deconvolution for Diffusion MRI. Proceedings of the International Society for Magnetic Resonance in Medicine. Seattle, Washington, USA; 2006. p. 2767.

    Google Scholar 

  40. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994;31(4):394–400.

    Article  CAS  PubMed  Google Scholar 

  41. Beaulieu C, Allen PS. Water diffusion in the giant axon of the squid: implications for diffusion-weighted MRI of the nervous system. Magn Reson Med. 1994;32(5):579–83.

    Article  CAS  PubMed  Google Scholar 

  42. Beaulieu C, Does MD, Snyder RE, Allen PS. Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med. 1996;36(4):627–31.

    Article  CAS  PubMed  Google Scholar 

  43. Song S-K, Sun S-W, Ju W-K, Lin S-J, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20(3):1714–22.

    Article  PubMed  Google Scholar 

  44. Song S-K, Sun S-W, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17(3):1429–36.

    Article  PubMed  Google Scholar 

  45. Dell’acqua F, Simmons A, Williams SCR, Catani M. Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true-tract specific index to characterize white matter diffusion. Human Brain Mapping [Internet]. 2012 Apr 5 [cited 2012 Apr 12]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/22488973

  46. Jbabdi S, Behrens TEJ, Smith SM. Crossing fibres in tract-based spatial statistics. Neuroimage. 2010;49(1):249–56.

    Article  PubMed  Google Scholar 

  47. Raffelt D, Tournier J-D, Rose S, Ridgway GR, Henderson R, Crozier S, et al. Apparent fibre density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage. 2012;59(4):3976–94.

    Article  PubMed  Google Scholar 

  48. Callaghan PT. NMR imaging, NMR diffraction and applications of pulsed gradient spin echoes in porous media. Magn Reson Imaging. 1996;14(7-8):701–9.

    Article  CAS  PubMed  Google Scholar 

  49. Wedeen VJ, Hagmann P, Tseng W-YI, Reese TG, Weisskoff RM. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med. 2005;54(6):1377–86.

    Article  PubMed  Google Scholar 

  50. Tuch DS. Q-ball imaging. Magn Reson Med. 2004;52(6):1358–72.

    Article  PubMed  Google Scholar 

  51. Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med. 2007;58(3):497–510.

    Article  PubMed  Google Scholar 

  52. Hess CP, Mukherjee P, Han ET, Xu D, Vigneron DB. Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med. 2006;56(1):104–17.

    Article  PubMed  Google Scholar 

  53. Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N. Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn Reson Med. 2010;64(2):554–66.

    PubMed Central  PubMed  Google Scholar 

  54. Özarslan E, Shepherd TM, Vemuri BC, Blackband SJ, Mareci TH. Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). Neuroimage. 2006;31(3):1086–103.

    Article  PubMed  Google Scholar 

  55. Jansons KM, Alexander DC. Persistent angular structure: new insights from diffusion magnetic resonance imaging data. Inverse Problems. 2003;19(5):1031–46.

    Article  Google Scholar 

  56. Yeh C-H, Tournier J-D, Cho K-H, Lin C-P, Calamante F, Connelly A. The effect of finite diffusion gradient pulse duration on fibre orientation estimation in diffusion MRI. Neuroimage. 2010;51(2):743–51.

    Article  PubMed  Google Scholar 

  57. Alexander DC, Seunarine KK. Mathematics of crossing fibers. In: Jones DK, editor. Diffusion MRI: theory, methods, and applications. New York, NY: Oxford University Press; 2010.

    Google Scholar 

  58. Descoteaux M, Deriche R, Knösche TR, Anwander A. Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans Med Imaging. 2009;28(2):269–86.

    Article  PubMed  Google Scholar 

  59. Tournier JD, Yeh C-H, Calamante F, Cho K-H, Connelly A, Lin C-P. Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage. 2008;42(2):617–25.

    Article  PubMed  Google Scholar 

  60. Zhan W, Yang Y. How accurately can the diffusion profiles indicate multiple fiber orientations? A study on general fiber crossings in diffusion MRI. J Magn Reson. 2006;183(2):193–202.

    Article  PubMed  Google Scholar 

  61. Tournier J-D, Calamante F, Connelly A. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 2013;26(12):1775–86.

    Article  PubMed  Google Scholar 

  62. Yamada K, Sakai K, Hoogenraad FGC, Holthuizen R, Akazawa K, Ito H, et al. Multitensor tractography enables better depiction of motor pathways: initial clinical experience using diffusion-weighted mr imaging with standard b-value. AJNR Am J Neuroradiol. 2007;28(9):1668–73.

    Article  CAS  PubMed  Google Scholar 

  63. Berman JI, Berger MS, Chung SW, Nagarajan SS, Henry RG. Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging. J Neurosurg. 2007;107(3):488.

    Article  PubMed  Google Scholar 

  64. Berman JI, Berger MS, Mukherjee P, Henry RG. Diffusion-tensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. J Neurosurg. 2004;101(1):66–72.

    Article  PubMed  Google Scholar 

  65. Clark CA, Barrick TR, Murphy MM, Bell BA. White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning? Neuroimage. 2003;20(3):1601–8.

    Article  PubMed  Google Scholar 

  66. Itoh D, Aoki S, Maruyama K, Masutani Y, Mori H, Masumoto T, et al. Corticospinal tracts by diffusion tensor tractography in patients with arteriovenous malformations. J Comput Assist Tomogr. 2006;30(4):618.

    Article  PubMed  Google Scholar 

  67. Mikuni N, Okada T, Enatsu R, Miki Y, Hanakawa T, Urayama S, et al. Clinical impact of integrated functional neuronavigation and subcortical electrical stimulation to preserve motor function during resection of brain tumors. J Neurosurg. 2007;106(4):593–8.

    Article  PubMed  Google Scholar 

  68. Okada T, Miki Y, Kikuta K, Mikuni N, Urayama S, Fushimi Y, et al. Diffusion tensor fiber tractography for arteriovenous malformations: quantitative analyses to evaluate the corticospinal tract and optic radiation. AJNR Am J Neuroradiol. 2007;28(6):1107–13.

    Article  CAS  PubMed  Google Scholar 

  69. Yamada K, Kizu O, Ito H, Nishimura T. Tractography for an arteriovenous malformation. Neurology. 2004;62(4):669.

    Article  PubMed  Google Scholar 

  70. Farquharson S, Tournier J-D, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI: clinical article. J Neurosurg. 2013;118(6):1367–77.

    Article  PubMed  Google Scholar 

  71. Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med. 1999;42(3):515–25.

    Article  CAS  PubMed  Google Scholar 

  72. Tournier J-D, Calamante F, Connelly A. How many diffusion gradient directions are required for HARDI? Proceedings of the International Society for Magnetic Resonance in Medicine. Hawai’i, USA; 2009. p. 358.

    Google Scholar 

  73. Winston GP, Daga P, Stretton J, Modat M, Symms MR, McEvoy AW, et al. Optic radiation tractography and vision in anterior temporal lobe resection. Ann Neurol. 2012;71(3):334–41.

    Article  PubMed Central  PubMed  Google Scholar 

  74. Ebeling U, Reulen H-J. Subcortical topography and proportions of the pyramidal tract. Acta Neurochir. 1992;118(3-4):164–71.

    Article  CAS  PubMed  Google Scholar 

  75. Nilsson D, Rydenhag B, Malmgren K, Starck G, Ljungberg M. Anatomical accuracy and feasibility of probabilistic and deterministic tractography of the optic radiation. Epilepsia [Internet]. 2010 [cited 2013 Aug 29]. p. 91. Available from: http://onlinelibrary.wiley.com.ezp.lib.unimelb.edu.au/ doi/10.1111/j.1528-1167.2010.02658.x/abstract

  76. Nimsky C, Ganslandt O, Merhof D, Sorensen AG, Fahlbusch R. Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking. Neuroimage. 2006;1:30(4).

    Google Scholar 

  77. Nimsky C, Ganslandt O, Fahlbusch R. Implementation of fiber tract navigation. Neurosurgery. 2007;61(1 Suppl):306–17. discussion 317–318.

    PubMed  Google Scholar 

  78. Wahl M, Strominger Z, Jeremy RJ, Barkovich AJ, Wakahiro M, Sherr EH, et al. Variability of homotopic and heterotopic callosal connectivity in partial agenesis of the corpus callosum: a 3T diffusion tensor imaging and q-ball tractography study. AJNR Am J Neuroradiol. 2009;30(2):282–9.

    Article  CAS  PubMed  Google Scholar 

  79. Daianu M, Jahanshad N, Dennis EL, Toga AW, McMahon KL, de Zubicaray GI, et al. Left versus right hemisphere differences in brain connectivity: 4-Tesla HARDI tractography in 569 twins. 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). 2012. p. 526–9.

    Google Scholar 

  80. Dennis EL, Jahanshad N, McMahon KL, de Zubicaray GI, Martin NG, Hickie IB, et al. Development of brain structural connectivity between ages 12 and 30: a 4-Tesla diffusion imaging study in 439 adolescents and adults. Neuroimage. 2013;64:671–84.

    Article  PubMed Central  PubMed  Google Scholar 

  81. Calamante F, Tournier J-D, Jackson GD, Connelly A. Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. Neuroimage. 2010;53(4):1233–43.

    Article  PubMed  Google Scholar 

  82. Zalesky A, Akhlaghi H, Corben LA, Bradshaw JL, Delatycki MB, Storey E, et al. Cerebello-cerebral connectivity deficits in Friedreich ataxia. Brain Struct Funct. 2014;219(3):969–81.

    Article  PubMed  Google Scholar 

  83. Raffelt D, Tournier J-D, Crozier S, Connelly A, Salvado O, Tournier J-D. Apparent Fibre Density: a new measure for high angular resolution diffusion-weighted image analysis. International Society for Magnetic Resonance in Medicine 14th Annual Scientific Meeting and Exhibition: 2006 Proceedings. Stockholm, Sweden; 2010.

    Google Scholar 

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Authors and Affiliations

  1. Florey Institute of Neuroscience and Mental Health, University of Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia

    Shawna Farquharson MSc & Jacques-Donald Tournier PhD

  2. Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK

    Jacques-Donald Tournier PhD

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Correspondence to Jacques-Donald Tournier PhD .

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Editors and Affiliations

  1. icometrix; Department of Radiology, Department of Radiology, Antwerp University Hospital, Leuven, Antwerpen, Belgium

    Wim Van Hecke

  2. Departments of Translational MRI and Radiology, KU Leuven and University Hospitals Leuven, Leuven, Belgium

    Louise Emsell

  3. Departments of Translational MRI and Radiology, KU Leuven and University Hospitals Leuven, Leuven, Belgium

    Stefan Sunaert

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Farquharson, S., Tournier, JD. (2016). High Angular Resolution Diffusion Imaging. In: Van Hecke, W., Emsell, L., Sunaert, S. (eds) Diffusion Tensor Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3118-7_20

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