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Incorporating non-linear alignment and multi-compartmental modeling for improved human optic nerve diffusion imaging.

Joo-Won Kim1, Jesper Lr Andersson2, Alan C Seifert1

  • 1Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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|April 2, 2019
PubMed
Summary

We developed a new algorithm to accurately image the human optic nerve using diffusion MRI (dMRI), overcoming challenges like nerve movement and fluid effects for better disease diagnosis.

Keywords:
Diffusion MRIMotion correctionMulti-compartmental modelingNon-linear registrationOptic nerve

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Radiology

Background:

  • In vivo human optic nerve diffusion MRI (dMRI) faces challenges including non-linear optic nerve movement and partial-volume effects from surrounding fluids.
  • Accurate microstructural characterization of the optic nerve is crucial for diagnosing neuropathies.

Purpose of the Study:

  • To develop a non-linear optic nerve registration algorithm for improved alignment in high-resolution optic nerve dMRI.
  • To characterize optic nerve microstructural parameters and motion along the posterior-to-anterior dimension using advanced dMRI models.

Main Methods:

  • Developed a non-linear optic nerve registration algorithm for axial dMRI data.
  • Acquired eyes-closed dMRI data including diffusion tensor imaging (DTI) with and without free water elimination (FWE) and diffusion basis spectrum imaging (DBSI).
  • Characterized optic nerve motion and microstructural parameters at various locations.

Main Results:

  • Optic nerve DTI showed consistent microstructural trends along the posterior-to-anterior axis, with the anterior portion exhibiting the largest displacement.
  • Multi-compartmental dMRI models (DTI with FWE, DBSI) reduced spatially dependent biases in diffusivity and anisotropy measurements.
  • DBSI results from a clinically feasible protocol (∼10 min) align with small animal study findings.

Conclusions:

  • The developed registration algorithm improves optic nerve dMRI volume alignment.
  • Multi-compartmental dMRI models offer more robust microstructural quantification in the optic nerve.
  • This approach provides a foundation for using advanced dMRI in human optic neuropathy research.