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DTI in Clinical Practice: Opportunities and Considerations

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Diffusion Tensor Imaging

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Abstract

DTI has become the primary imaging method of choice for investigating white matter microstructure in a preclinical research context, with wide-ranging neurological applications. It is less widely used to investigate nonbrain tissue, where it can be useful in assessing skeletal muscle and peripheral nerves. Despite its widespread adoption as a research tool, DTI is not used routinely in clinical practice.

In this introductory chapter we offer the inexperienced (pre-)clinical DTI user a high-level overview of potential preclinical applications and the types of things that could be considered before embarking on DTI data-collection and analysis in preclinical populations. Such considerations are discussed in the context of three main themes: Who will be scanned? (characteristics of the patient population), How will they be scanned? (scanning, hardware and software resources), and Who can help? (human resources).

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References

  1. Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am J Neuroradiol. 2008;29(4):632–41 [Review].

    Article  CAS  PubMed  Google Scholar 

  2. van Hecke W, Nagels G, Emonds G, Leemans A, Sijbers J, van Goethem J, et al. A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions. J Magn Reson Imaging. 2009;30(1):25–34 [Evaluation Studies].

    Article  PubMed  Google Scholar 

  3. Vedantam A, Jirjis MB, Schmit BD, Wang MC, Ulmer JL, Kurpad SN. Diffusion tensor imaging of the spinal cord: insights from animal and human studies. Neurosurgery. 2014;74(1):1–8.

    Article  PubMed  Google Scholar 

  4. Zhou Y, Narayana PA, Kumaravel M, Athar P, Patel VS, Sheikh KA. High resolution diffusion tensor imaging of human nerves in forearm. J Magn Reson Imaging. 2014;39(6):1374–83.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Khalil C, Budzik JF, Kermarrec E, Balbi V, Le Thuc V, Cotten A. Tractography of peripheral nerves and skeletal muscles. Eur J Radiol. 2010;76(3):391–7 [Review].

    Article  CAS  PubMed  Google Scholar 

  6. Wei H, Viallon M, Delattre BM, Wang L, Pai VM, Wen H, et al. Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo. IEEE Trans Med Imaging. 2013;32(10):1928–38 [Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  7. Furman-Haran E, Eyal E, Shapiro-Feinberg M, Nissan N, Grobgeld D, Weisenberg N, et al. Advantages and drawbacks of breast DTI. Eur J Radiol. 2012;81 Suppl 1:S45–7.

    Article  PubMed  Google Scholar 

  8. Tagliafico A, Rescinito G, Monetti F, Villa A, Chiesa F, Fisci E, et al. Diffusion tensor magnetic resonance imaging of the normal breast: reproducibility of DTI-derived fractional anisotropy and apparent diffusion coefficient at 3.0 T. Radiol Med. 2012;117(6):992–1003.

    Article  CAS  PubMed  Google Scholar 

  9. Froeling M, Nederveen AJ, Nicolay K, Strijkers GJ. DTI of human skeletal muscle: the effects of diffusion encoding parameters, signal-to-noise ratio and T2 on tensor indices and fiber tracts. NMR Biomed. 2013;26(11):1339–52.

    Article  PubMed  Google Scholar 

  10. Jaimes C, Darge K, Khrichenko D, Carson RH, Berman JI. Diffusion tensor imaging and tractography of the kidney in children: feasibility and preliminary experience. Pediatr Radiol. 2014;44(1):30–41.

    Article  PubMed  Google Scholar 

  11. Notohamiprodjo M, Dietrich O, Horger W, Horng A, Helck AD, Herrmann KA, et al. Diffusion tensor imaging (DTI) of the kidney at 3 tesla-feasibility, protocol evaluation and comparison to 1.5 Tesla. Invest Radiol. 2010;45(5):245–54 [Comparative Study].

    Article  PubMed  Google Scholar 

  12. Kim CK, Jang SM, Park BK. Diffusion tensor imaging of normal prostate at 3 T: effect of number of diffusion-encoding directions on quantitation and image quality. Br J Radiol. 2012;85(1015):e279–83 [Comparative Study].

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Gurses B, Kabakci N, Kovanlikaya A, Firat Z, Bayram A, Ulug AM, et al. Diffusion tensor imaging of the normal prostate at 3 Tesla. Eur Radiol. 2008;18(4):716–21.

    Article  PubMed  Google Scholar 

  14. Fiocchi F, Nocetti L, Siopis E, Curra S, Costi T, Ligabue G, et al. In vivo 3 T MR diffusion tensor imaging for detection of the fibre architecture of the human uterus: a feasibility and quantitative study. Br J Radiol. 2012;85(1019):e1009–17.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Verhoeven JS, Sage CA, Leemans A, Van Hecke W, Callaert D, Peeters R, et al. Construction of a stereotaxic DTI atlas with full diffusion tensor information for studying white matter maturation from childhood to adolescence using tractography-based segmentations. Hum Brain Mapp. 2010;31(3):470–86 [Research Support, Non-U.S. Gov't].

    PubMed  Google Scholar 

  16. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage. 2012;60(1):340–52 [Research Support, Non-U.S. Gov't].

    Article  CAS  PubMed  Google Scholar 

  17. Kasprian G, Brugger PC, Weber M, Krssak M, Krampl E, Herold C, et al. In utero tractography of fetal white matter development. Neuroimage. 2008;43(2):213–24.

    Article  PubMed  Google Scholar 

  18. Huang H, Xue R, Zhang J, Ren T, Richards LJ, Yarowsky P, et al. Anatomical characterization of human fetal brain development with diffusion tensor magnetic resonance imaging. J Neurosci. 2009;29(13):4263–73 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't].

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Zhan J, Dinov ID, Li J, Zhang Z, Hobel S, Shi Y, et al. Spatial-temporal atlas of human fetal brain development during the early second trimester. Neuroimage. 2013;82:115–26 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  20. Deipolyi AR, Mukherjee P, Gill K, Henry RG, Partridge SC, Veeraraghavan S, et al. Comparing microstructural and macrostructural development of the cerebral cortex in premature newborns: diffusion tensor imaging versus cortical gyration. Neuroimage. 2005;27(3):579–86 [Comparative Study Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.].

    Article  PubMed  Google Scholar 

  21. Judas M. Prenatal development of the human fetal telencephalon. In: Prayer D, editor. Fetal MRI. Heidelberg, Germany: Springer; 2011. p. 81–146.

    Google Scholar 

  22. Huang H, Zhang J, Wakana S, Zhang W, Ren T, Richards LJ, et al. White and gray matter development in human fetal, newborn and pediatric brains. Neuroimage. 2006;33(1):27–38 [Research Support, N.I.H., Extramural].

    Article  PubMed  Google Scholar 

  23. Hüppi P. Diffusion tensor imaging in brain development. In: Jones DK, editor. Diffusion MRI: theory, methods, and applications. New York, NY: Oxford University Press, Inc.; 2010.

    Google Scholar 

  24. Lenroot RK, Giedd JN. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30(6):718–29 [Review].

    Article  PubMed  Google Scholar 

  25. Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Huppi PS, Hertz-Pannier L. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience. 2014;276:48–71.

    Article  CAS  PubMed  Google Scholar 

  26. Faria AV, Zhang J, Oishi K, Li X, Jiang H, Akhter K, et al. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage. 2010;52(2):415–28 [Comparative Study Research Support, N.I.H., Extramural].

    Article  PubMed Central  PubMed  Google Scholar 

  27. Brickman AM, Meier IB, Korgaonkar MS, Provenzano FA, Grieve SM, Siedlecki KL, et al. Testing the white matter retrogenesis hypothesis of cognitive aging. Neurobiol Aging. 2012;33(8):1699–715 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't].

    Article  PubMed Central  PubMed  Google Scholar 

  28. Sullivan EV, Pfefferbaum A. DTI in aging and age-related neurodegenerative disorders. In: Jones DK, editor. Diffusion MRI: theory, methods and applications. New York: Springer; 2011.

    Google Scholar 

  29. Maillard P, Carmichael O, Harvey D, Fletcher E, Reed B, Mungas D, et al. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol. 2013;34(1):54–61 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't].

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Fazekas F, Schmidt R, Scheltens P. Pathophysiologic mechanisms in the development of age-related white matter changes of the brain. Dement Geriatr Cogn Disord. 1998;9 Suppl 1:2–5 [Review].

    Article  PubMed  Google Scholar 

  31. de Groot M, Verhaaren BF, de Boer R, Klein S, Hofman A, van der Lugt A, et al. Changes in normal-appearing white matter precede development of white matter lesions. Stroke. 2013;44(4):1037–42 [Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  32. Bartzokis G, Tishler TA, Lu PH, Villablanca P, Altshuler LL, Carter M, et al. Brain ferritin iron may influence age- and gender-related risks of neurodegeneration. Neurobiol Aging. 2007;28(3):414–23 [Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.].

    Article  CAS  PubMed  Google Scholar 

  33. Pfefferbaum A, Adalsteinsson E, Rohlfing T, Sullivan EV. Diffusion tensor imaging of deep gray matter brain structures: effects of age and iron concentration. Neurobiol Aging. 2010;31(3):482–93 [Research Support, N.I.H., Extramural].

    Article  PubMed Central  PubMed  Google Scholar 

  34. Chang LC, Walker L, Pierpaoli C. Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magn Reson Med. 2012;68(5):1654–63 [Research Support, N.I.H., Intramural Research Support, U.S. Gov't, Non-P.H.S.].

    Article  PubMed Central  PubMed  Google Scholar 

  35. Gholipour A, Kehtarnavaz N, Scherrer B, Warfield SK. On the accuracy of unwarping techniques for the correction of susceptibility-induced geometric distortion in magnetic resonance Echo-planar images. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:6997–7000 [Research Support, N.I.H., Extramural].

    PubMed Central  PubMed  Google Scholar 

  36. Vos SB, Jones DK, Viergever MA, Leemans A. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage. 2011;55(4):1566–76 [Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  37. Cook PA, Symms M, Boulby PA, Alexander DC. Optimal acquisition orders of diffusion-weighted MRI measurements. J Magn Reson Imaging. 2007;25(5):1051–8 [Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  38. Dubois J, Poupon C, Lethimonnier F, Le Bihan D. Optimized diffusion gradient orientation schemes for corrupted clinical DTI data sets. MAGMA. 2006;19(3):134–43 [Evaluation Studies].

    Article  CAS  PubMed  Google Scholar 

  39. Thorpe S, Salkovskis PM, Dittner A. Claustrophobia in MRI: the role of cognitions. Magn Reson Imaging. 2008;26(8):1081–8.

    Article  PubMed  Google Scholar 

  40. Grey SJ, Price G, Mathews A. Reduction of anxiety during MR imaging: a controlled trial. Magn Reson Imaging. 2000;18(3):351–5 [Clinical Trial Controlled Clinical Trial Research Support, Non-U.S. Gov't].

    Article  CAS  PubMed  Google Scholar 

  41. Raschle N, Zuk J, Ortiz-Mantilla S, Sliva DD, Franceschi A, Grant PE, et al. Pediatric neuroimaging in early childhood and infancy: challenges and practical guidelines. Ann N Y Acad Sci. 2012;1252:43–50 [Research Support, Non-U.S. Gov't Review].

    Article  PubMed Central  PubMed  Google Scholar 

  42. Farquharson S, Tournier JD, 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(6):1367–77 [Case Reports Comparative Study Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  43. Bello L, Gambini A, Castellano A, Carrabba G, Acerbi F, Fava E, et al. Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. Neuroimage. 2008;39(1):369–82 [Clinical Trial Research Support, Non-U.S. Gov't].

    Article  PubMed  Google Scholar 

  44. Illes J, Kirschen MP, Edwards E, Bandettini P, Cho MK, Ford PJ, et al. Practical approaches to incidental findings in brain imaging research. Neurology. 2008;70(5):384–90 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Review].

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Rangel EK. The management of incidental findings in neuro-imaging research: framework and recommendations. J Law Med Ethics. 2010;38(1):117–26.

    Article  PubMed  Google Scholar 

  46. Brakewood B, Poldrack RA. The ethics of secondary data analysis: considering the application of Belmont principles to the sharing of neuroimaging data. Neuroimage. 2013;82:671–6.

    Article  PubMed  Google Scholar 

  47. Polders DL, Leemans A, Hendrikse J, Donahue MJ, Luijten PR, Hoogduin JM. Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla. J Magn Reson Imaging. 2011;33(6):1456–63.

    Article  PubMed  Google Scholar 

  48. Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol. 2008;29(5):843–52 [Review].

    Article  CAS  PubMed  Google Scholar 

  49. Maclaren J, Herbst M, Speck O, Zaitsev M. Prospective motion correction in brain imaging: a review. Magn Reson Med. 2013;69(3):621–36 [Research Support, Non-U.S. Gov't Review].

    Article  PubMed  Google Scholar 

  50. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45(2):265–9 [Research Support, Non-U.S. Gov't].

    Article  CAS  PubMed  Google Scholar 

  51. Burgel U, Madler B, Honey CR, Thron A, Gilsbach J, Coenen VA. Fiber tracking with distinct software tools results in a clear diversity in anatomical fiber tract portrayal. Cent Eur Neurosurg. 2009;70(1):27–35 [Case Reports Research Support, Non-U.S. Gov't].

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Translational MRI, Department of Imaging and Pathology, KU Leuven, and Radiology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium

    Louise Emsell PhD & Stefan Sunaert MD, PhD

  2. Universitair Psychiatrisch Centrum (UPC)- KU Leuven, Leuven, Belgium

    Louise Emsell PhD

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  1. Louise Emsell PhD
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  2. Stefan Sunaert MD, PhD
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Correspondence to Louise Emsell 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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Emsell, L., Sunaert, S. (2016). DTI in Clinical Practice: Opportunities and Considerations. 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_13

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  • DOI: https://doi.org/10.1007/978-1-4939-3118-7_13

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-3117-0

  • Online ISBN: 978-1-4939-3118-7

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