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Abstract

Diffusion tensor imaging (DTI) is sensitive to micron scale displacement of water molecules, providing unique insight into microstructural tissue architecture. The tensors provide a compact way to describe the average of these displacements that occur within a voxel. However, current practical image resolution is in the millimeter scale, and thus diffusivities from many tissue compartments are averaged in each voxel, reducing the specificity of the measurement to subtle pathologies. In this chapter we review the free-water model, and use it to derive diffusion tensors following the elimination of the free-water component, that is assumed to originate from the extracellular space. Doing so, the resulting diffusion tensors and their derived indices measure the tissue itself, and are more sensitive to the geometry of the tissue, increasing the specificity to pathologies that affect brain tissue.

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References

  1. Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994)

    Article  Google Scholar 

  2. Assaf, Y., Pasternak, O.: Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J. Mol. Neurosci. 34(1), 51–61 (2008). doi:10.1007/s12031-007-0029-0

    Article  Google Scholar 

  3. Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S.: Diffusion Tensor Imaging of the Brain. Neurotherapeutics 4(3), 316–329 (2007). doi:10.1016/j.nurt.2007.05.011

    Article  Google Scholar 

  4. Vos, S.B., Jones, D.K., Viergever, M.A., Leemans, A.: Partial volume effect as a hidden covariate in DTI analyses. NeuroImage 55(4), 1566–1576 (2011). doi:10.1016/j.neuroimage.2011.01.048

    Article  Google Scholar 

  5. Pasternak, O., Assaf, Y., Intrator, N., Sochen, N.: Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels. Magn. Reson. Imaging 26(8), 1133–1144 (2008)

    Article  Google Scholar 

  6. Malcolm, J.G., Shenton, M.E., Rathi, Y.: Filtered multi-tensor tractography. IEEE Trans. Med. Imaging 29, 1664–1675 (2010). doi:10.1109/TMI.2010.2048121

    Article  Google Scholar 

  7. Alexander, D.: Multiple-fibre reconstruction algorithms for diffusion MRI. Ann. N. Y. Acad. Sci. 1046, 113–133 (2005)

    Article  Google Scholar 

  8. Jones, D.K., Cercignani, M.: Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 23(7), 803–820 (2010). doi:10.1002/nbm.1543

    Article  Google Scholar 

  9. Pasternak, O., Sochen, N., Gur, Y., Intrator, N., Assaf, Y.: Free water elimination and mapping from diffusion mri. Magn. Reson. Med. 62(3), 717–730 (2009)

    Article  Google Scholar 

  10. Metzler-Baddeley, C., O’Sullivan, M.J., Bells, S., Pasternak, O., Jones, D.K.: How and how not to correct for CSF-contamination in diffusion MRI. NeuroImage 59(2), 1394–1403 (2012). doi:10.1016/j.neuroimage.2011.08.043

    Article  Google Scholar 

  11. Wang, Y., Wang, Q., Haldar, J.P., Yeh, F.C., Xie, M., Sun, P., Tu, T.W., Trinkaus, K., Klein, R.S., Cross, A.H., Song, S.K.: Quantification of increased cellularity during inflammatory demyelination. Brain 134(12), 3590–3601 (2011). doi:10.1093/brain/awr307

    Article  Google Scholar 

  12. Metzler-Baddeley, C., Jones, D., Belaroussi, B., Aggleton, J., O’Sullivan, M.: Frontotemporal connections in episodic memory and aging: A diffusion MRI tractography study. J. Neurosci. 31(37), 13236–13245 (2011)

    Article  Google Scholar 

  13. Pasternak, O., Westin, C.F., Bouix, S., Seidman, L.J., Goldstein, J.M., Woo, T.U.W., Petryshen, T.L., Mesholam-Gately, R.I., McCarley, R.W., Kikinis, R., et al.: Excessive extracellular volume reveals a neurodegenerative pattern in schizophrenia onset. J. Neurosci. 32(48), 17365–17372 (2012)

    Article  Google Scholar 

  14. Fritzsche, K., Stieltjes, B., van Bruggen, T., Meinzer, H.P., Westin, C.F., Pasternak, O.: A combined approach for the elimination of partial volume effects in diffusion MRI. In: Proceedings of the 20th ISMRM, Melbourne, p. 3548 (2012)

    Google Scholar 

  15. Harris, K.R., Woolf, L.A.: Pressure and temperature dependence of the self diffusion coefficient of water and oxygen-18 water. J. Chem. Soc. Faraday Trans. 1 76, 377–385 (1980). doi:10.1039/ F19807600377, http://dx.doi.org/10.1039/F19807600377

  16. Pierpaoli, C., Jones, D.: Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model. In: Proceedings of the 12th ISMRM, Kyoto, p. 1215 (2004)

    Google Scholar 

  17. Mulkern, R.V., Haker, S.J., Maier, S.E.: On high b diffusion imaging in the human brain: ruminations and experimental insights. Magn. Reson. Imaging 27(8), 1151–1162 (2009). doi:10.1016/j.mri.2009.05.003

    Article  Google Scholar 

  18. Behrens, T.E., Woolrich, M.W., Jenkinson, M., Johansen-Berg, H., Nunes, R.G., Clare, S., Matthews, P.M., Brady, J.M., Smith, S.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50(5), 1077–1088 (2003)

    Article  Google Scholar 

  19. Pierpaoli, C., Jezzard, P., Basser, P., Barnett, A., Di Chiro, G.: Diffusion tensor MR imaging of the human brain. Radiology 201(3), 637–648 (1996)

    Article  Google Scholar 

  20. Gur, Y., Pasternak, O., Sochen, N.: Fast GL(n)-invariant framework for tensors regularization. Int. J. Comput. Vis. 85(3), 211–222 (2009)

    Article  Google Scholar 

  21. Pasternak, O., Sochen, N., Basser, P.J.: The effect of metric selection on the analysis of diffusion tensor MRI data. NeuroImage 49(3), 2190–2204 (2010). doi:10.1016/ j.neuroimage.2009.10.071, http://dx.doi.org/10.1016/j.neuroimage.2009.10.071

  22. Jones, D.K.: The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a monte carlo study. Magn. Reson. Med. 51(4), 807–815 (2004)

    Article  Google Scholar 

  23. Pasternak, O., Shenton, M., Westin, C.F.: Estimation of extracellular volume from regularized multi-shell diffusion MRI. In: Proceedings of the MICCAI, Nice, pp. 305–312 (2012)

    Google Scholar 

  24. Westin, C.F., Pasternak, O., Knutsson, H.: Rotationally invariant gradient schemes for diffusion MRI. In: Proceedings of the 20th ISMRM, Melbourne (2012)

    Google Scholar 

  25. King, M.D., Gadian, D.G., Clark, C.A.: A random effects modelling approach to the crossing-fibre problem in tractography. NeuroImage 44, 753–768 (2009)

    Article  Google Scholar 

  26. Baumgartner, C., Michailovich, O., Levitt, J., Pasternak, O., Bouix, S., Westin, C., Rathi, Y.: A unified tractography framework for comparing diffusion models on clinical scans. In: Computational Diffusion MRI Workshop of MICCAI, Nice, pp. 27–32 (2012)

    Google Scholar 

  27. Metzler-Baddeley, C., O’Sullivan, M.J., Bells, S., Pasternak, O., Jones, D.K.: How and how not to correct for CSF-contamination in diffusion MRI. NeuroImage 59(2), 1394–1403 (2012). doi:10.1016/j.neuroimage.2011.08.043

    Article  Google Scholar 

  28. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.J.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31(4), 1487–1505 (2006). doi:10.1016/j.neuroimage.2006.02.024, http://dx.doi.org/10.1016/j.neuroimage.2006.02.024

  29. Schlueter, M., Stieltjes, B., Hahn, H.K., Rexilius, J., Konrad-verse, O., Peitgen, H.O.: Detection of tumour infiltration in axonal fibre bundles using diffusion tensor imaging. Int. J. Med. Robot. 1(3), 80–86 (2005). doi:10.1002/rcs.31, http://dx.doi.org/10.1002/rcs.31

  30. Stieltjes, B., Schlüter, M., Didinger, B., Weber, M.A., Hahn, H.K., Parzer, P., Rexilius, J., Konrad-Verse, O., Peitgen, H.O., Essig, M.: Diffusion tensor imaging in primary brain tumors: reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model. NeuroImage 31(2), 531–542 (2006). doi:10.1016/ j.neuroimage.2005.12.052, http://dx.doi.org/10.1016/j.neuroimage.2005.12.052

  31. Noe, A., Gee, J.C.: Partial volume segmentation of cerebral mri scans with mixture model clustering. In: IPMI, Davis, pp. 423–430 (2001)

    Google Scholar 

  32. McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley Series in Probability and Statistics (2007). doi:10.1002/9780470191613

    Google Scholar 

  33. Laidlaw, D.H., Fleischer, K.W., Barr, A.H.: Partial-volume bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Trans. Med. Imaging 17(1), 74–86 (1998). doi:10.1109/42.668696, http://dx.doi.org/10.1109/42.668696

  34. Weiner, H.L., Selkoe, D.J.: Inflammation and therapeutic vaccination in CNS diseases. Nature 420(6917), 879–884 (2002). doi:10.1038/nature01325, http://www.nature.com/nature/journal/v420/n6917/full/nature01325.html

  35. Agosta, F., Pievani, M., Sala, S., Geroldi, C., Galluzzi, S., Frisoni, G.B., Filippi, M.: White matter damage in alzheimer disease and its relationship to gray matter atrophy. Radiology 258(3), 853–863 (2011). doi:10.1148/radiol.10101284, http://radiology.rsna.org/content/258/3/853.long

  36. Assaf, Y., Basser, P.J.: Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage 27(1), 48–58 (2005). doi:10.1016/j.neuroimage.2005.03.042, http://www.sciencedirect.com/science/article/pii/S1053811905002259

  37. Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y., Basser, P.J.: Axcaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59(6), 1347–1354 (2008)

    Article  Google Scholar 

  38. Zhang, H., Schneider, T., Wheeler-Kingshott, C.A.M., Alexander, D.C.: NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000–1016 (2012)

    Article  Google Scholar 

  39. van Bruggen, T., Zhang, H., Pasternak, O., Meinzer, H.P., Stieltjes, B., Fritzsche, K.H.: Free-water elimination for assessing microstructural gray matter pathology - with application to alzheimer’s disease. In: Proceedings of the 21th ISMRM, Salt Lake City, p. 790 (2013)

    Google Scholar 

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Acknowledgements

This work was supported by the following grants: Department of Defense X81XWH-07-CC-CSDoD; NIH P41RR013218, P41EB015902, NIH R01MH074794; VA Merit Award. OP is partly supported by a National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Grant from the Brain and Behavior Research Foundation.

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Correspondence to Ofer Pasternak .

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Pasternak, O., Maier-Hein, K., Baumgartner, C., Shenton, M.E., Rathi, Y., Westin, CF. (2014). The Estimation of Free-Water Corrected Diffusion Tensors. In: Westin, CF., Vilanova, A., Burgeth, B. (eds) Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54301-2_11

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