Visualization of human optic nerve by diffusion tensor mapping and degree of neuropathy

Abstract

Diffusion-weighted magnetic resonance imaging of the human optic nerve and tract is technically difficult because of its small size, the inherent strong signal generated by the surrounding fat and the cerebrospinal fluid, and due to eddy current-induced distortions and subject movement artifacts. The effects of the bone canal through which the optic nerve passes, and the proximity of blood vessels, muscles and tendons are generally unknown. Also, the limited technical capabilities of the scanners and the minimization of acquisition times result in poor quality diffusion-weighted images. It is challenging for current tractography methods to accurately track optic pathway fibers that correspond to known anatomy. Despite these technical limitations and low image resolution, here we show how to visualize the optic nerve and tract and quantify nerve atrophy. Our visualization method based on the analysis of the diffusion tensor shows marked differences between a healthy male subject and a male subject with progressive optic nerve neuropathy. These differences coincide with diffusion scalar metrics and are not visible on standard morphological images. A quantification of the degree of optic nerve atrophy in a systematic way is provided and it is tested on 9 subjects from the Human Connectome Project.

Fig 1

A schematic anatomical overview of optic pathway and surroundings, a–muscles and tendons, b–optic nerve, c–nearby bony structures (optic canal), d–optic tract, e–eyeball, f–blood vessels, g–optic chiasm, h–ophthalmic artery, i–Meyer’s loop, j–lateral geniculate nucleus, k–optic radiation and visual cortex.

Fig 2

The visual effect of the algorithm in six steps for a healthy subject, a–principal diffusion directions shown as line segments, b–length coding, c–opacity coding, d–color coding, e–seven layers projection, f–seven layers projection and data thresholding. In a-d a single layer was used. White arrow: oculomotor nerve. Orientation convention: Radiological.

Fig 3. Visual pathway shape on different plane projections.

White and blue arrows indicate right and left optic nerve, respectively. Real voxel proportions are preserved.

Fig 4

Visual pathway of male subject with right optic nerve atrophy (orange arrows) (a) and for healthy male subject (b) visualized with the new DTI algorithm superimposed on the same T1 image. Optic nerves are also presented on diffusion scalar maps FA and MD, and on morphological T1-weighted images. Actual values of FA and MD measured in a region of interest containing the optic nerve are presented in green. Blue arrows: optic canal and locations of visible discontinuities. Orientation convention: radiological. Values of α, β, γ and σ used in visualization were 250, 1000, 1.2 and 0.2, respectively.

Fig 5

Images for male subject with right optic nerve neuropathy, generated for σ values equal to A) 0, B) 0.15, C) 0.25, and D) 0.35. Density plots in the bottom panels show all voxels in the datasets used for visualization (black dots) and voxels omitted according to condition (7) (grey dots). The dots are obtained from all voxels in the selected layers. The red dashed lines correspond to the condition L_1N = RD_N+σ. The left optic nerve (white arrow) is visible (A,B,C) until a certain value of σ is exceeded (D). Orientation convention: Radiological.