Advertisement

Simulating Patient Specific Multiple Time-Point MRIs from a Biophysical Model of Brain Deformation in Alzheimer’s Disease

  • Bishesh Khanal
  • Marco Lorenzi
  • Nicholas Ayache
  • Xavier Pennec
Conference paper

Abstract

This paper proposes a framework to simulate patient specific structural Magnetic Resonance Images (MRIs) from the available MRI scans of Alzheimer’s Disease (AD) subjects. We use a biophysical model of brain deformation due to atrophy that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI. Large number of brain regions are segmented in 45 AD patients and the atrophy rates per year are estimated in these regions from the available two extremal time-point scans. Assuming linear progression of atrophy, the volume changes in scans closest to the half way time period are computed. These atrophy maps are prescribed to the baseline images to simulate the middle time-point images by using the biophysical model of brain deformation. From the baseline scans, the volume changes in real middle time-point scans are compared to the ones in simulated middle time-point images. This present framework also allows to introduce desired atrophy patterns at different time points to simulate non-linear progression of atrophy. This opens a way to use a biophysical model of brain deformation to evaluate methods that study the temporal progression and spatial relationships of atrophy of different regions in the brain with AD.

Keywords

Alzheimer’s disease Biophysical modeling Biomechanical simulation 

Notes

Acknowledgements

Part of this work was funded by the European Research Council through the ERC Advanced Grant MedYMA 2011-291080.

References

  1. 1.
    S. Balay, J. Brown, K. Buschelman, W.D. Gropp, D. Kaushik, M.G. Knepley, L.C. McInnes, B.F. Smith, H. Zhang, PETSc Web page (2013), http://www.mcs.anl.gov/petsc
  2. 2.
    H. Braak, E. Braak, Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991)CrossRefGoogle Scholar
  3. 3.
    J. Brown, M.G. Knepley, D.A. May, L.C. McInnes, B. Smith, Composable linear solvers for multiphysics, in 2012 11th International Symposium on Parallel and Distributed Computing (ISPDC) (2012), pp. 55–62Google Scholar
  4. 4.
    O. Camara, M. Schweiger, R.I. Scahill, W.R. Crum, B.I. Sneller, J.A. Schnabel, G.R. Ridgway, D.M. Cash, D.L.G. Hill, N.C. Fox, Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med. Imaging 25(11), 1417–1430 (2006)CrossRefGoogle Scholar
  5. 5.
    O. Camara, R.I. Scahill, J.A. Schnabel, W.R. Crum, G.R. Ridgway, D.L.G. Hill, N.C. Fox, Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal data, in MICCAI 2007, ed. by N. Ayache, S. Ourselin, A. Maeder. Lecture Notes in Computer Science, vol. 4792 (Springer, Heidelberg, 2007), pp. 785–792Google Scholar
  6. 6.
    O. Carmichael, D.G. McLaren, D. Tommet, D. Mungas, R.N. Jones, Coevolution of brain structures in amnestic mild cognitive impairment. NeuroImage 66, 449–456 (2013)CrossRefGoogle Scholar
  7. 7.
    B. Fischl, D.H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, A.M. Dale, Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  8. 8.
    H.M. Fonteijn, M. Modat, M.J. Clarkson, J. Barnes, M. Lehmann, N.Z. Hobbs, R.I. Scahill, S.J. Tabrizi, S. Ourselin, N.C. Fox, D.C. Alexander, An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease. NeuroImage 60(3), 1880–1889 (2012)CrossRefGoogle Scholar
  9. 9.
    G.B. Frisoni, N.C. Fox, C.R. Jack, P. Scheltens, P.M. Thompson, The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)CrossRefGoogle Scholar
  10. 10.
    R.T. Johnson, C.J. Gibbs Jr., Creutzfeldt–Jakob disease and related transmissible spongiform encephalopathies. N. Engl. J. Med. 339(27), 1994–2004 (1998)CrossRefGoogle Scholar
  11. 11.
    B. Karaçali, C. Davatzikos, Simulation of tissue atrophy using a topology preserving transformation model. IEEE Trans. Med. Imaging 25(5), 649–652 (2006)CrossRefGoogle Scholar
  12. 12.
    B. Khanal, M. Lorenzi, N. Ayache, X. Pennec, A biophysical model of shape changes due to atrophy in the brain with Alzheimer’s disease, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, ed. by P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe. Lecture Notes in Computer Science, vol. 8674 (Springer, Cham, 2014), pp. 41–48Google Scholar
  13. 13.
    I.B. Malone, D. Cash, G.R. Ridgway, D.G. MacManus, S. Ourselin, N.C. Fox, J.M. Schott, MIRIAD–Public release of a multiple time point Alzheimer’s MR imaging dataset. NeuroImage 70, 33–36 (2013)CrossRefGoogle Scholar
  14. 14.
    P. Pieperhoff, M. Südmeyer, L. Hömke, K. Zilles, A. Schnitzler, K. Amunts, Detection of structural changes of the human brain in longitudinally acquired MR images by deformation field morphometry: methodological analysis, validation and application. NeuroImage 43(2), 269–287 (2008)CrossRefGoogle Scholar
  15. 15.
    M. Reuter, N.J. Schmansky, H.D. Rosas, B. Fischl, Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61(4), 1402–1418 (2012)CrossRefGoogle Scholar
  16. 16.
    S. Sharma, V. Noblet, F. Rousseau, F. Heitz, L. Rumbach, J.P. Armspach, Evaluation of brain atrophy estimation algorithms using simulated ground-truth data. Med. Image Anal. 14(3), 373–389 (2010)CrossRefGoogle Scholar
  17. 17.
    S. Sharma, F. Rousseau, F. Heitz, L. Rumbach, J.P. Armspach, On the estimation and correction of bias in local atrophy estimations using example atrophy simulations. Comput. Med. Imaging Graph. 37(7–8), 538–551 (2013)CrossRefGoogle Scholar
  18. 18.
    A.D.C. Smith, W.R. Crum, D.L. Hill, N.A. Thacker, P.A. Bromiley, Biomechanical simulation of atrophy in MR images, in Medical Imaging 2003. International Society for Optics and Photonics (2003), pp. 481–490Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bishesh Khanal
    • 1
  • Marco Lorenzi
    • 2
    • 3
  • Nicholas Ayache
    • 1
  • Xavier Pennec
    • 1
  1. 1.Inria Sophia Antipolis Méditerranée, Asclepios Research ProjectSophia AntipolisFrance
  2. 2.University College London, Translational Imaging GroupLondonUK
  3. 3.INRIA Sophia Antipolis Méditerranée, Asclepios Research ProjectSophia AntipolisFrance

Personalised recommendations