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Asymmetry Spectrum Imaging for Baby Diffusion Tractography

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Information Processing in Medical Imaging (IPMI 2019)

Abstract

Fiber tractography in baby diffusion MRI is challenging due to the low and spatially-varying diffusion anisotropy, causing most tractography algorithms to yield streamlines that fall short of reaching the cortex. In this paper, we introduce a method called asymmetry spectrum imaging (ASI) to improve the estimation of white matter pathways in the baby brain by (i) incorporating an asymmetric fiber orientation model to resolve subvoxel fiber configurations such as fanning and bending, and (ii) explicitly modeling the range (or spectrum) of typical diffusion length scales in the developing brain. We validated ASI using in-vivo baby diffusion MRI data from the Baby Connectome Project (BCP), demonstrating that ASI can characterize complex subvoxel fiber configurations and accurately estimate the fiber orientation distribution function in spite of changes in diffusion patterns. This, in turn, results in significantly better diffusion tractography in the baby brain.

This work was supported in part by NIH grants (NS093842, EB022880, MH104324 and 1U01MH110274), a research grant from Nestec Ltd., and the efforts of the UNC/UMN Baby Connectome Project Consortium.

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References

  1. Johansen-Berg, H., Behrens, T.E.: Diffusion MRI: From Quantitative Measurement to in Vivo Neuroanatomy. Academic Press, Cambridge (2013)

    Google Scholar 

  2. Bihan, D.L.: Molecular diffusion, tissue microdynamics and microstructure. NMR Biomed. 8, 375–386 (1995)

    Article  Google Scholar 

  3. Jeurissen, B., Tournier, J.D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014)

    Article  Google Scholar 

  4. Cetin, S., Ozarslan, E., Unal, G.: Elucidating intravoxel geometry in diffusion-MRI: asymmetric orientation distribution functions (AODFs) revealed by a cone model. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 231–238. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_29

    Chapter  Google Scholar 

  5. Bastiani, M., et al.: Improved tractography using asymmetric fibre orientation distributions. NeuroImage 158, 205–218 (2017)

    Article  Google Scholar 

  6. Reisert, M., Kellner, E., Kiselev, V.G.: About the geometry of asymmetric fiber orientation distributions. IEEE Trans. Med. Imaging 31(6), 1240–1249 (2012)

    Article  Google Scholar 

  7. Parker, G., Marshall, D., Rosin, P.L., Drage, N., Richmond, S., Jones, D.K.: A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data. Neuroimage 65, 433–448 (2013)

    Article  Google Scholar 

  8. Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)

    Article  Google Scholar 

  9. Auría, A., Daducci, A., Thiran, J.P., Wiaux, Y.: Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI. NeuroImage 115, 245–255 (2015)

    Article  Google Scholar 

  10. Wu, Y., Feng, Y., Shen, D., Yap, P.-T.: A multi-tissue global estimation framework for asymmetric fiber orientation distributions. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 45–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_6

    Chapter  Google Scholar 

  11. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

    Article  Google Scholar 

  12. Caruyer, E., Daducci, A., Descoteaux, M., Houde, J.C., Thiran, J.P., Verma, R.: Phantomas: a flexible software library to simulate diffusion MR phantoms. In: ISMRM (2014)

    Google Scholar 

  13. Dhollander, T., Raffelt, D., Connelly, A.: Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. In: ISMRM Workshop on Breaking the Barriers of Diffusion MRI, vol. 5 (2016)

    Google Scholar 

  14. Bastiani, M., et al.: Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. NeuroImage 185, 750–763 (2018)

    Article  Google Scholar 

  15. Garyfallidis, E., et al.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinf. 8, 8 (2014)

    Article  Google Scholar 

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Correspondence to Pew-Thian Yap .

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Wu, Y., Lin, W., Shen, D., Yap, PT., and the UNC/UMN Baby Connectome Project Consortium. (2019). Asymmetry Spectrum Imaging for Baby Diffusion Tractography. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_24

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  • DOI: https://doi.org/10.1007/978-3-030-20351-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20350-4

  • Online ISBN: 978-3-030-20351-1

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