Advertisement

Analysis of Morphological Changes of Lamina Cribrosa Under Acute Intraocular Pressure Change

  • Mathilde RavierEmail author
  • Sungmin Hong
  • Charly Girot
  • Hiroshi Ishikawa
  • Jenna Tauber
  • Gadi Wollstein
  • Joel Schuman
  • James Fishbaugh
  • Guido Gerig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Glaucoma is the second leading cause of blindness worldwide. Despite active research efforts driven by the importance of diagnosis and treatment of the optic degenerative neuropathy, the relationship between structural and functional changes along the glaucomateous evolution are still not clearly understood. Dynamic changes of the lamina cribrosa (LC) in the presence of intraocular pressure (IOP) were suggested to play a significant role in optic nerve damage, which motivates the proposed research to explore the relationship of changes of the 3D structure of the LC collagen meshwork to clinical diagnosis. We introduce a framework to quantify 3D dynamic morphological changes of the LC under acute IOP changes in a series of swept-source optical coherence tomography (SS-OCT) scans taken under different pressure states. Analysis of SS-OCT images faces challenges due to low signal-to-noise ratio, anisotropic resolution, and observation variability caused by subject and ocular motions. We adapt unbiased diffeomorphic atlas building which serves multiple purposes critical for this analysis. Analysis of deformation fields yields desired global and local information on pressure-induced geometric changes. Deformation variability, estimated with repeated images of a healthy volunteer without IOP elevation, is found to be a magnitude smaller than pressure-induced changes and thus illustrates feasibility of the proposed framework. Results in a clinical study with healthy, glaucoma suspect, and glaucoma subjects demonstrate the potential of the proposed method for non-invasive in vivo analysis of LC dynamics, potentially leading to early prediction and diagnosis of glaucoma.

Notes

Acknowledgements

This research was supported by NIH R01-EY013178 and EY025011.

References

  1. 1.
    Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTs). Insight J. 2, 1–35 (2009)Google Scholar
  2. 2.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Girot, C., Ishikawa, H., Fishbaugh, J., Wollstein, G., Schuman, J., Gerig, G.: Spatiotemporal analysis of structural changes of the lamina cribrosa. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA-2017. LNCS, vol. 10554, pp. 185–193. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67561-9_21CrossRefGoogle Scholar
  4. 4.
    Inoue, R., et al.: Three-dimensional high-speed optical coherence tomography imaging of lamina cribrosa in glaucoma. Ophthalmology 116(2), 214–222 (2009)CrossRefGoogle Scholar
  5. 5.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, S151–S160 (2004)CrossRefGoogle Scholar
  6. 6.
    Lee, E.J., Choi, Y.J., Kim, T.W., Hwang, J.M.: Comparison of the deep optic nerve head structure between normal-tension glaucoma and nonarteritic anterior ischemic optic neuropathy. PloS ONE 11(4), e0150242 (2016)CrossRefGoogle Scholar
  7. 7.
    Nadler, Z., et al.: In vivo three-dimensional characterization of the healthy human lamina cribrosa with adaptive optics spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 55(10), 6459–6466 (2014)CrossRefGoogle Scholar
  8. 8.
    Ortiz, S., et al.: Optical distortion correction in optical coherence tomography for quantitative ocular anterior segment by three-dimensional imaging. Opt. Express 18(3), 2782–2796 (2010)CrossRefGoogle Scholar
  9. 9.
    Quigley, H.A., Broman, A.T.: The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90(3), 262–267 (2006)CrossRefGoogle Scholar
  10. 10.
    Sigal, I.A., Wang, B., Strouthidis, N.G., Akagi, T., Girard, M.J.: Recent advances in OCT imaging of the lamina cribrosa. Br. J. Ophthalmol. 98(Suppl 2), ii34–ii39 (2014)CrossRefGoogle Scholar
  11. 11.
    Unser, M., Aldroubi, A., Eden, M.: Fast B-spline transforms for continuous image representation and interpolation. IEEE Trans. Patt. Anal. Mach. Intell. 13(3), 277–285 (1991)CrossRefGoogle Scholar
  12. 12.
    Wollstein, G., et al.: Optical coherence tomography longitudinal evaluation of retinal nerve fiber layer thickness in glaucoma. Arch. Ophthalmol. 123(4), 464–470 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mathilde Ravier
    • 1
    Email author
  • Sungmin Hong
    • 1
  • Charly Girot
    • 2
  • Hiroshi Ishikawa
    • 3
  • Jenna Tauber
    • 3
  • Gadi Wollstein
    • 3
  • Joel Schuman
    • 3
  • James Fishbaugh
    • 1
  • Guido Gerig
    • 1
  1. 1.Department of Computer ScienceTandon School of EngineeringBrooklynUSA
  2. 2.Department of Computer ScienceCPE Lyon School of EngineeringLyonFrance
  3. 3.Department of OphthalmologyLangone Medical CenterNew York CityUSA

Personalised recommendations