Advertisement

Simulating Neurodegeneration through Longitudinal Population Analysis of Structural and Diffusion Weighted MRI Data

  • Marc Modat
  • Ivor J. A. Simpson
  • Manual Jorge Cardoso
  • David M. Cash
  • Nicolas Toussaint
  • Nick C. Fox
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Neuroimaging biomarkers play a prominent role for disease diagnosis or tracking neurodegenerative processes. Multiple methods have been proposed by the community to extract robust disease specific markers from various imaging modalities. Evaluating the accuracy and robustness of developed methods is difficult due to the lack of a biologically realistic ground truth.

We propose a proof-of-concept method for a patient- and disease-specific brain neurodegeneration simulator. The proposed scheme, based on longitudinal multi-modal data, has been applied to a population of normal controls and patients diagnosed with Alzheimer’s disease or frontotemporal dementia. We simulated follow-up images from baseline scans and compared them to real repeat images. Additionally, simulated maps of volume change are generated, which can be compared to maps estimated from real longitudinal data. The results indicate that the proposed simulator reproduces realistic patient-specific patterns of longitudinal brain change for the given populations.

Keywords

Fractional Anisotropy Normal Control Longitudinal Change Frontotemporal Dementia Template Database 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Smith, S.M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P.M., Federico, A., De Stefano, N.: Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage 17(1), 479–489 (2002)CrossRefGoogle Scholar
  2. 2.
    Leung, K.K., Ridgway, G.R., Ourselin, S., Fox, N.C., Initiative, A.D.N.: Consistent multi-time-point brain atrophy estimation from the boundary shift integral. NeuroImage 59(4), 3995–4005 (2012)CrossRefGoogle Scholar
  3. 3.
    Chung, M., Worsley, K.J., Paus, T., Cherif, C., Collins, D.L., Giedd, J.N., Rapoport, J.L., Evans, A.C.: A unified statistical approach to deformation-based morphometry. NeuroImage 14(3), 595–606 (2001)CrossRefGoogle Scholar
  4. 4.
    Davatzikos, C., Genc, A., Xu, D., Resnick, S.M.: Voxel-based morphometry using the ravens maps: methods and validation using simulated longitudinal atrophy. NeuroImage 14(6), 1361–1369 (2001)CrossRefGoogle Scholar
  5. 5.
    Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D.L.G., Fox, N.: Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Transactions on Medical Imaging 25(11), 1417–1430 (2006)CrossRefGoogle Scholar
  6. 6.
    Sharma, S., Rousseau, F., Heitz, F., Rumbach, L., Armspach, J.P.: On the estimation and correction of bias in local atrophy estimations using example atrophy simulations. Computerized Medical Imaging and Graphics 37(7-8), 538–551 (2013)CrossRefGoogle Scholar
  7. 7.
    Avants, B.B., Duda, J.T., Zhang, H., Gee, J.C.: Multivariate normalization with symmetric diffeomorphisms for multivariate studies. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 359–366. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Studholme, C.: Dense feature deformation morphometry: Incorporating DTI data into conventional MRI morphometry. Medical Image Analysis 12(6), 742–751 (2008)CrossRefGoogle Scholar
  9. 9.
    Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  10. 10.
    Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Meth. Prog. Bio. 98(3), 278–284 (2010)CrossRefGoogle Scholar
  11. 11.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Cachier, P., Bardinet, E., Dormont, D., Pennec, X., Ayache, N.: Iconic feature based nonrigid registration: the PASHA algorithm. Computer Vision and Image Understanding 89(2-3), 272–298 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis 10(5), 764–785 (2006)CrossRefGoogle Scholar
  14. 14.
    Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de Solorzano, C.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Transactions on Medical Imaging 28(8), 1266–1277 (2009)CrossRefGoogle Scholar
  15. 15.
    Lorenzi, M., Pennec, X.: Efficient parallel transport of deformations in time series of images: From schilds to pole ladder. Journal of Mathematical Imaging and Vision, 1–13 (October 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marc Modat
    • 1
    • 2
  • Ivor J. A. Simpson
    • 1
    • 2
  • Manual Jorge Cardoso
    • 1
    • 2
  • David M. Cash
    • 2
    • 1
  • Nicolas Toussaint
    • 1
    • 2
  • Nick C. Fox
    • 2
  • Sébastien Ourselin
    • 1
    • 2
  1. 1.Translational Imaging Group, Centre for Medical Imaging Computing, Department of Medical Physics and BioengineeringUniversity College LondonUK
  2. 2.Dementia Research Centre, Institute of NeurologyUniversity College LondonQueen SquareUK

Personalised recommendations