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Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14227))

Abstract

Most diffusion biophysical models capture basic properties of tissue microstructure, such as diffusivity and anisotropy. More realistic models that relate the diffusion-weighted signal to cell size and membrane permeability often require simplifying assumptions such as short gradient pulse and Gaussian phase distribution, leading to tissue features that are not necessarily quantitative. Here, we propose a method to quantify tissue microstructure without jeopardizing accuracy owing to unrealistic assumptions. Our method utilizes realistic signals simulated from the geometries of cellular microenvironments as fingerprints, which are then employed in a spherical mean estimation framework to disentangle the effects of orientation dispersion from microscopic tissue properties. We demonstrate the efficacy of microstructure fingerprinting in estimating intra-cellular, extra-cellular, and intra-soma volume fractions as well as axon radius, soma radius, and membrane permeability.

This work was supported in part by the United States National Institutes of Health (NIH) through grants MH125479 and EB008374.

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References

  1. Hall, M.G., Alexander, D.C.: Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI. IEEE Trans. Med. Imaging 28(9), 1354–1364 (2009)

    Article  Google Scholar 

  2. Balls, G., Frank, L.: A simulation environment for diffusion weighted MR experiments in complex media. Magn. Reson. Med. 62(3), 771–778 (2009)

    Article  Google Scholar 

  3. Li, J.R., et al.: SpinDoctor: a Matlab toolbox for diffusion MRI simulation. Neuroimage 202, 116120 (2019)

    Article  Google Scholar 

  4. Fang, C., Nguyen, V.D., Wassermann, D., Li, J.R.: Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module. Neuroimage 222, 117198 (2020)

    Article  Google Scholar 

  5. Rensonnet, G., et al.: Towards microstructure fingerprinting: estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations. Neuroimage 184, 964–980 (2019)

    Article  Google Scholar 

  6. Nedjati-Gilani, G.L., et al.: Machine learning based compartment models with permeability for white matter microstructure imaging. Neuroimage 150, 119–135 (2017)

    Article  Google Scholar 

  7. Nilsson, M., Alerstam, E., Wirestam, R., Sta, F., Brockstedt, S., Lätt, J., et al.: Evaluating the accuracy and precision of a two-compartment Kärger model using Monte Carlo simulations. J. Magn. Reson. 206(1), 59–67 (2010)

    Article  Google Scholar 

  8. Huynh, K.M., et al.: Probing tissue microarchitecture of the baby brain via spherical mean spectrum imaging. IEEE Trans. Med. Imaging 39(11), 3607–3618 (2020)

    Article  Google Scholar 

  9. Veraart, J., et al.: Noninvasive quantification of axon radii using diffusion MRI. eLIFE 9, e49855 (2020)

    Google Scholar 

  10. Frigo, M., Fick, R., Zucchelli, M., Deslauriers-Gauthier, S., Deriche, R.: Multi tissue modelling of diffusion MRI signal reveals volume fraction bias. In: International Symposium on Biomedical Imaging. (2020)

    Google Scholar 

  11. Huynh, K.M., et al.: Probing brain micro-architecture by orientation distribution invariant identification of diffusion compartments. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 547–555 (2019)

    Google Scholar 

  12. Aboitiz, F., Scheibel, A.B., Fisher, R.S., Zaidel, E.: Fiber composition of the human corpus callosum. Brain Res. 598(1–2), 143–153 (1992)

    Article  Google Scholar 

  13. Fan, Q., et al.: Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI. NeuroImage 222 117197 (2020)

    Google Scholar 

  14. Imae, T., et al.: Estimation of cell membrane permeability and intracellular diffusion coefficient of human gray matter. Magn. Reson. Med. Sci. 8(1), 1–7 (2009)

    Article  Google Scholar 

  15. Veraart, J., Raven, E.P., Edwards, L.J., Weiskopf, N., Jones, D.K.: The variability of MR axon radii estimates in the human white matter. Hum. Brain Mapp. 42(7), 2201–2213 (2021)

    Article  Google Scholar 

  16. Walter, A., Gutknecht, J.: Permeability of small nonelectrolytes through lipid bilayer membranes. J. Membr. Biol. 90(3), 207–217 (1986)

    Article  Google Scholar 

  17. Zhang, H., Hubbard, P.L., Parker, G.J., Alexander, D.C.: Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. Neuroimage 56(3), 1301–1315 (2011)

    Article  Google Scholar 

  18. White, N.S., Leergaard, T.B., D’Arceuil, H., Bjaalie, J.G., Dale, A.M.: Probing tissue microstructure with restriction spectrum imaging: histological and theoretical validation. Hum. Brain Mapp. 34(2), 327–346 (2013)

    Article  Google Scholar 

  19. Palombo, M., et al.: SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. NeuroImage 215 116835 (2020)

    Google Scholar 

  20. Huynh, K.M., et al.: Characterizing intra-soma diffusion with spherical mean spectrum imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 354–363 (2020)

    Google Scholar 

  21. Daducci, A., Canales-Rodríguez, E.J., Zhang, H., Dyrby, T.B., Alexander, D.C., Thiran, J.P.: Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage 105, 32–44 (2015)

    Article  Google Scholar 

  22. Kaden, E., Kruggel, F., Alexander, D.C.: Quantitative mapping of the per-axon diffusion coefficients in brain white matter. Magn. Reson. Med. 75(4), 1752–1763 (2016)

    Article  Google Scholar 

  23. 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 

  24. Afzali, M., Nilsson, M., Palombo, M., Jones, D.K.: Spheriously? the challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. Neuroimage 237, 118183 (2021)

    Article  Google Scholar 

  25. Kaden, E., Kelm, N.D., Carson, R.P., Does, M.D., Alexander, D.C.: Multi-compartment microscopic diffusion imaging. Neuroimage 139, 346–359 (2016)

    Article  Google Scholar 

  26. Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)

    Article  Google Scholar 

  27. Alexander, D.C., et al.: Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage 52(4), 1374–1389 (2010)

    Article  Google Scholar 

  28. Drobnjak, I., Neher, P., Poupon, C., Sarwar, T.: Physical and digital phantoms for validating tractography and assessing artifacts. Neuroimage 245, 118704 (2021)

    Article  Google Scholar 

  29. Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    Google Scholar 

  30. Liewald, D., Miller, R., Logothetis, N., Wagner, H.-J., Schüz, A.: Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque. Biol. Cybern. 108(5), 541–557 (2014). https://doi.org/10.1007/s00422-014-0626-2

    Article  Google Scholar 

  31. Sepehrband, F., Alexander, D.C., Kurniawan, N.D., Reutens, D.C., Yang, Z.: Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI. NMR Biomed. 29(3), 293–308 (2016)

    Article  Google Scholar 

  32. Caminiti, R., Ghaziri, H., Galuske, R., Hof, P.R., Innocenti, G.M.: Evolution amplified processing with temporally dispersed slow neuronal connectivity in primates. Proc. Natl. Acad. Sci. 106(46), 19551–19556 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, University of North Carolina, Chapel Hill, USA

    Khoi Minh Huynh, Ye Wu, Sahar Ahmad & Pew-Thian Yap

  2. Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA

    Khoi Minh Huynh, Ye Wu, Sahar Ahmad & Pew-Thian Yap

Authors
  1. Khoi Minh Huynh
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  2. Ye Wu
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  3. Sahar Ahmad
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  4. Pew-Thian Yap
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Corresponding author

Correspondence to Pew-Thian Yap .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Huynh, K.M., Wu, Y., Ahmad, S., Yap, PT. (2023). Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-43993-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43992-6

  • Online ISBN: 978-3-031-43993-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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