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Diffusion MRI Fiber Tractography by Flow Field Formation with Extended Physarum Solver: A Pilot Study with 2D Phantoms

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Computational Diffusion MRI

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

Information of the brain white matter fiber bundle structures is obtained by diffusion MRI (dMRI) analysis and is already indispensable for medical science and clinical medicine. Since the fundamental technique for tractography was presented about twenty years ago, various methodologies have been developed and reported. However, this problem leaves room for technical improvement, especially for application in clinical dMRI data of limited quality. In this study, a novel approach based on the physarum solver was investigated. Through the experiments on synthetic and real data sets, potentials and limitations of the approach were displayed and discussed.

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References

  1. Jeurissen, B., Descoteaux, M., Mori, S., Leemans, A.: Diffusion MRI fiber tractography of the brain. NMR Biomed. 32(4), e3785 (2019)

    Article  Google Scholar 

  2. Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2), 265–269 (1999)

    Article  Google Scholar 

  3. Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007)

    Article  Google Scholar 

  4. Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. Neuroimage 54(2), 955–962 (2011)

    Article  Google Scholar 

  5. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23(3), 1176–1185 (2004)

    Article  Google Scholar 

  6. Neher, P.F., Götz, M., Norajitra, T., Weber, C., Maier-Hein, K.H.: A machine learning based approach to fiber tractography using classifier voting. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A. (eds.) MICCAI 2015 (Part I), LNCS, vol. 9349, pp. 45–52. Springer, Cham (2015)

    Google Scholar 

  7. Koppers, S., Merhof, D.: Direct estimation of fiber orientations using deep learning in diffusion imaging. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.I. (eds.) MLMI 2016, LNCS, vol. 10019, pp. 53–60. Springer, Cham (2016)

    Google Scholar 

  8. Tero, A., Kobayashi, R., Nakagaki, T.: Physarum solver: a biologically inspired method of road-network navigation. Phys. Stat. Mech. Appl. 363(1), 115–119 (2006)

    Google Scholar 

  9. Nakagaki, T., Yamada, H., Toth, A.: Maze-solving by an amoeboid organism. Nature 407, 470 (2000)

    Article  Google Scholar 

  10. Kershaw, D.S.: The incomplete Cholesky-conjugate gradient method for the iterative solution of systems of linear equations. J. Comput. Phys. 26(1), 43–65 (1978)

    Article  MathSciNet  Google Scholar 

  11. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)

    Article  Google Scholar 

  12. Faber, T.E.: Fluid Dynamics for Physicists. Cambridge University Press, Cambridge (1995)

    Google Scholar 

  13. Poupon, C., Rieul, B., Kezele, I., Perrin, M., Poupon, F., Mangin, J.F.: New diffusion phantoms dedicated to the study and validation of HARDI models. Magn. Reson. Med. 60, 1276–1283 (2008)

    Article  Google Scholar 

  14. Original Fibercup—Data. http://www.tractometer.org/original_fibercup/data/. Last accessed 29 Aug 2019

  15. Sotiropoulos, S.N., Bai, L., Morgan, P.S., Constantinescu, C.S., Tench, C.R.: Brain tractography using Q-Ball imaging and graph theory: improved connectivities through fibre crossings via a model-based approach. Neuroimage 49(3), 2444–2456 (2010)

    Article  Google Scholar 

  16. Brown, C.J., Booth, B.G., Hamarneh, G.: K-confidence: assessing uncertainty in tractography using K optimal paths. In: Proceedings IEEE 10th International Symposium on Biomedical Imaging, pp. 250–253 (2013)

    Google Scholar 

  17. Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S.: Probabilistic shortest path tractography in DTI using gaussian process ODE solvers. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014 (Part III), LNCS, vol. 8675, pp. 265–272. Springer, Cham (2014)

    Google Scholar 

  18. Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.: A random riemannian metric for probabilistic shortest-path tractography. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015 (Part I), LNCS, vol. 9349, pp. 597–604. Springer, Cham (2015)

    Google Scholar 

  19. O’Donnell, L., Haker, S., Westin, C.F.: New approaches to estimation of white matter connectivity in diffusion tensor MRI: Elliptic PDEs and geodesics in a tensor-warped space. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002 (Part I), LNCS, vol. 2488, pp. 459–466. Springer, Heidelberg (2002)

    Google Scholar 

  20. Parker, G.J.M., Wheeler-Kingshott, C.A.M., Barker, G.J.: Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imging 21(5), 505–512 (2002)

    Google Scholar 

  21. Fuster, A., Dela Haije, T., Tristán-Vega, A., Plantinga, B., Westin, C.F., Florack, L.: Adjugate diffusion tensors for geodesic tractography in white matter. J. Math. Imging Vis. 54, 1–14 (2016)

    Google Scholar 

  22. Hageman, N.S., Toga, A.W., Narr, K., Shattuck, D.W.: A diffusion tensor imaging tractography algorithm based on Navier-Stokes fluid mechanics. IEEE Trans. Med. Imging 28(3), 348–360 (2010)

    Google Scholar 

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Acknowledgments

The author is grateful to Prof. Atsushi Tero in Kyushu University, Japan and his group for the valuable discussion and advice on the physarum solver. This research was partially supported by JST CREST Grant Number JPMJCR15D1.

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Authors and Affiliations

  1. Hiroshima City University, Hiroshima, Japan

    Yoshitaka Masutani

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  1. Yoshitaka Masutani
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Correspondence to Yoshitaka Masutani .

Editor information

Editors and Affiliations

  1. Centre for Medical Imaging and Centre for Medical Image Computing, University College London, London, UK

    Elisenda Bonet-Carne

  2. Centre for Medical Engineering, King’s College London, London, UK

    Jana Hutter

  3. Centre for Medical Image Computing, University College London, London, UK

    Marco Palombo

  4. Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Marco Pizzolato

  5. Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA

    Farshid Sepehrband

  6. Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA

    Fan Zhang

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Masutani, Y. (2020). Diffusion MRI Fiber Tractography by Flow Field Formation with Extended Physarum Solver: A Pilot Study with 2D Phantoms. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_16

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