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MesoFT: Unifying Diffusion Modelling and Fiber Tracking

  • Marco Reisert
  • V. G. Kiselev
  • Bibek Dihtal
  • Elias Kellner
  • D. S. Novikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

One overarching challenge of clinical magnetic resonance imaging (MRI) is to quantify tissue structure at the cellular scale of micrometers, based on an MRI acquisition with a millimeter resolution. Diffusion MRI (dMRI) provides the strongest sensitivity to the cellular structure. However, interpreting dMRI measurements has remained a highly ill-posed inverse problem. Here we propose a framework that resolves the above challenge for human white matter fibers, by unifying intra-voxel mesoscopic modeling with global fiber tractography. Our algorithm is based on a Simulated Annealing approach which simultaneously optimizes diffusion parameters and fiber locations. Each fiber carries its individual set of diffusion parameters which allows to link them by their structural relationships.

Keywords

Magnetic Resonance Imaging Acquisition Simulated Annealing Approach Clinical Magnetic Resonance Imaging Reversible Jump Monte Carlo Markov Chain Fiber Tractography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Alexander, D.C., Hubbard, P.L., Hall, M.G., Moore, E.A., Ptito, M., Parker, G.J.M., Dyrby, T.B.: Orientationally invariant indices of axon diameter and density from diffusion mri. NeuroImage 52, 1374–1389 (2010)CrossRefGoogle Scholar
  2. 2.
    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, 1347–1354 (2008)CrossRefGoogle Scholar
  3. 3.
    Bells, S., Cercignani, M., Deoni, S., Assaf, Y., Pasternak, O., Evans, C.J., Leemans, A., Jones, D.K.: Tractometry–comprehensive multi-modal quantitative assessment of white matter along specific tracts. In: Proc. ISMRM, vol. 678 (2011)Google Scholar
  4. 4.
    Fieremans, E., Jensen, J.H., Helpern, J.A.: White matter characterization with diffusional kurtosis imaging. Neuroimage 58(1), 177–188 (2011)CrossRefGoogle Scholar
  5. 5.
    Jones, D.K. (ed.): Diffusion MRI: Theory, Methods and Applications. Oxford University Press (2010)Google Scholar
  6. 6.
    Malcolm, J.G., Shenton, M.E., Rathi, Y.: Filtered multitensor tractography. IEEE Transactions on Medical Imaging 29(9), 1664–1675 (2010)CrossRefGoogle Scholar
  7. 7.
    Mangin, J.: A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted mr data - a technical review. NMR Biomed. 15(7-8), 481–492 (2002)CrossRefGoogle Scholar
  8. 8.
    Panagiotaki, E., Schneider, T., Siow, B., Hall, M.G., Lythgoe, M.F., Alexander, D.C.: Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59(3), 2241–2254 (2012)CrossRefGoogle Scholar
  9. 9.
    Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. Neuroimage 54(2), 955–962 (2011)CrossRefGoogle Scholar
  10. 10.
    Sherbondy, A.J., Rowe, M.C., Alexander, D.C.: Microtrack: an algorithm for concurrent projectome and microstructure estimation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 183–190. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marco Reisert
    • 1
  • V. G. Kiselev
    • 1
  • Bibek Dihtal
    • 1
  • Elias Kellner
    • 1
  • D. S. Novikov
    • 2
  1. 1.Department of Diagnostic Radiology, Medical PhysicsUniversity Medical Center FreiburgFreiburgGermany
  2. 2.Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUSA

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