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Anatomical Connectivity in the Central Nervous System Revealed by Diffusion Tensor Magnetic Resonance Imaging (DT-MRI)

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Quantitative Neuroscience

Part of the book series: Biocomputing ((BCOM,volume 2))

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Abstract

This work explains the basic principles of diffusion weighted and diffusion tensor magnetic resonance imaging (MRI) methods. Both theoretical and experimental aspects are included. Scalar measures derived from diffusion tensors including new anisotropy measures in terms of entropy are presented. Fiber-tract mapping problem is discussed. Limitations of diffusion tensor MRI are included. Directional coherence tensor formalism to smooth the diffusion tensor data is presented. A brief discussion of new approaches to diffusion imaging, such as diffusion spectrum imaging and high angular resolution diffusion imaging, are also discussed. Results from diffusion weighted and diffusion tensor imaging are presented from excised rat spinal cord and brain images acquired at 17.6T.

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References

  1. Bahn, M. M. (1999). Invariant and orthonormal scalar measures derived from magnetic resonance diffusion tensor imaging. J Magn Reson, 141: 68–77.

    Article  Google Scholar 

  2. Basser, P. J. (1995). Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed, 8 (7–8): 333–344.

    Article  Google Scholar 

  3. Basser, P. J. (2002). Relationships between diffusion tensor and q-space MRI. Magn. Reson. Med., 47: 392–397.

    Article  Google Scholar 

  4. Basser, P. J., Mattiello, J., and LeBihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson., B(103): 247–254.

    Article  Google Scholar 

  5. Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J., and Aldroubi, A. (2000). In vivo fiber tractography using DT-MRI data. Magn. Reson. Med., 44: 625–632.

    Article  Google Scholar 

  6. Beaulieu, C. and Allen, P. S. (1994). Determinants of anisotropic water diffusion in nerves. Magn Reson Med, 31 (4): 394–400.

    Article  Google Scholar 

  7. Bloch, F. (1946). Nuclear induction. Phys. Rev., 70 (7,8): 460–474.

    Article  Google Scholar 

  8. Callaghan, P. T. (1991). Principles of Nuclear Magnetic Resonance Microscopy. Oxford: Clarendon Press.

    Google Scholar 

  9. Chandrasekhar, S. (1943). Stochastic problems in physics and astronomy. Rev. Mod. Phys., 15 (1): 1–89.

    Article  MathSciNet  MATH  Google Scholar 

  10. Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., and Raichle, M. E. (1999). Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Set, 96: 10422–10427.

    Article  Google Scholar 

  11. Fick, E. and Sauermann, G. (1990). The Quantum Statistics of Dynamic Processes. New York: Springer-Verlag.

    Google Scholar 

  12. Frank, L. R. (2002). Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn Reson Med, 47 (6): 1083–1099.

    Article  Google Scholar 

  13. Hahn, E. L. (1950). Spin echoes. Phys. Rev., 80: 580–594.

    Article  MATH  Google Scholar 

  14. Hsu, E. W. and Mori, S. (1995). Analytical expressions for the NMR apparent diffusion coefficients in an anisotropic system and a simplified method for determining fiber orientation. Magn. Reson. Med., 34: 194–200.

    Article  Google Scholar 

  15. Mori, S., Crain, B. J., Chacko, V. P., and van Zijl, P. C. M. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol., 45: 265–269.

    Article  Google Scholar 

  16. Moseley, M. E., Cohen, Y., Mintorovitch, J., Chileuitt, L., Shimizu, H., Kucharczyk, J., Wendland, M. F., and Weinstein, P. R. (1990). Early detection of regional cerebral ischemia in cats: comparison of diffusion and T 2-weighted MRI and spectroscopy. Magn. Reson. Med., 14: 330–346.

    Article  Google Scholar 

  17. Özarslan, E., DeFord, S. M., Mareci, T. H., and Hayes, R. L. (2001). Mapping white matter fiber tracts with magnetic resonance imaging in rat traumatic brain injury. J. Neurotrauma, 18: 1126.

    Google Scholar 

  18. Özarslan, E. and Mareci, T. H. (2002). Directional coherence tensor formalism for diffusion tensor MRI: Mapping structure in spinal cord. gray-matter. In Proc. of the 10th Annual Meeting of ISMRM.

    Google Scholar 

  19. Özarslan, E. and Mareci, T. H. (2003a). Anisotropy as a certainty measure in terms of entropy. In Proc. of the 11th Annual Meeting of IMRM accepted.

    Google Scholar 

  20. Özarslan, E. and Mareci, T. H. (2003b). Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Magn Reson Med, accepted.

    Google Scholar 

  21. Özarslan, E. and Mareci, T. H. (2003c). On the relationships between diffusion tensor, high angular resolution diffusion and q-space imaging methods. In 44 th Experimental Nuclear Magnetic Resonance Conference,pages 267–268.

    Google Scholar 

  22. Stejskal, E. O. (1965). Use of spin echoes in a pulsed magnetic-field gradient to study anisotropic, restricted diffusion and flow. J. Chem. Phys., 43 (10): 3597–3603.

    Article  Google Scholar 

  23. Stejskal, E. O. and Tanner, J. E. (1965). Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys., 42 (1): 288–292.

    Article  Google Scholar 

  24. Torrey, H. C. (1956). Bloch equations with diffusion terms. Phys. Rev., 104 (3): 563–565.

    Article  Google Scholar 

  25. Tuch, D. S., Weisskoff, R. M., Belliveau, J. W., and Wedeen, V. J. (1999). High angular resolution diffusion imaging of the human brain. In Proc. of the 7th Annual Meeting of ISMRM, Philadelphia, page 321.

    Google Scholar 

  26. Waxman, S. G., Kocsis, J. D., and Stys, P. K. (1995). The Axon: structure, function, and pathophysiology. New York: Oxford University Press.

    Google Scholar 

  27. Wedeen, V. J., Reese, T. G., Tuch, D. S., Weigel, M. R., Dou, J. G., Weiskoff, R. M., and Chessler, D. (2000). Mapping fiber orientation spectra in cerebral white matter with Fourier transform diffusion MRI. In Proc. of the 8th Annual Meeting of ISMRM, Denver, page 82.

    Google Scholar 

  28. Wehrl, A. (1978). General properties of entropy. Rev. Mod. Phys., 50 (2): 221–260.

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Departments of Physics and Biomedical Engineering McKnight Brain Institute, University of Florida, Gainesville, FL, 32611, USA

    Evren Özarslan

  2. Departments of Biochemistry & Molecular Biology, Physics and Biomedical Engineering McKnight Brain Institute, University of Florida, Gainesville, FL, 32611, USA

    Thomas H. Mareci

Authors
  1. Evren Özarslan
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  2. Thomas H. Mareci
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Editor information

Editors and Affiliations

  1. Departments of Industrial and Systems Engineering, Biomedical Engineering, and McKnight Brain Institute, University of Florida, USA

    P. M. Pardalos

  2. Departments of Neuroscience, Neurology, Pediatrics, and Biomedical Engineering, University of Florida; VA Medical Center, USA

    J. C. Sackellares

  3. Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, USA

    P. R. Carney

  4. Department of Biomedical Engineering, Arizona State University, USA

    L. D. Iasemidis

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© 2004 Kluwer Academic Publishers

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Özarslan, E., Mareci, T.H. (2004). Anatomical Connectivity in the Central Nervous System Revealed by Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In: Pardalos, P.M., Sackellares, J.C., Carney, P.R., Iasemidis, L.D. (eds) Quantitative Neuroscience. Biocomputing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0225-4_8

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  • DOI: https://doi.org/10.1007/978-1-4613-0225-4_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7951-5

  • Online ISBN: 978-1-4613-0225-4

  • eBook Packages: Springer Book Archive

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