Tractography

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File:Deterministic Tractography of the Adult Brachial Plexus using Diffusion Tensor Imaging.gif

Image:DTI-sagittal-fibers.jpg

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Tractography is performed using data from diffusion MRI. The free water diffusion is termed "isotropic" diffusion. If the water diffuses in a medium with barriers, the diffusion will be uneven, which is termed anisotropic diffusion. In such a case, the relative mobility of the molecules from the origin has a shape different from a sphere. This shape is often modeled as an ellipsoid, and the technique is then called diffusion tensor imaging.{{cite journal | vauthors = Basser PJ, Mattiello J, LeBihan D | title = MR diffusion tensor spectroscopy and imaging | journal = Biophysical Journal | volume = 66 | issue = 1 | pages = 259–267 | date = January 1994 | pmid = 8130344 | pmc = 1275686 | doi = 10.1016/S0006-3495(94)80775-1 | bibcode = 1994BpJ....66..259B }} Barriers can be many things: cell membranes, axons, myelin, etc.; but in white matter the principal barrier is the myelin sheath of axons. Bundles of axons provide a barrier to perpendicular diffusion and a path for parallel diffusion along the orientation of the fibers.

Anisotropic diffusion is expected to be increased in areas of high mature axonal order. Conditions where the myelin or the structure of the axon are disrupted, such as trauma,{{cite journal | vauthors = Wade RG, Tanner SF, Teh I, Ridgway JP, Shelley D, Chaka B, Rankine JJ, Andersson G, Wiberg M, Bourke G | title = Diffusion Tensor Imaging for Diagnosing Root Avulsions in Traumatic Adult Brachial Plexus Injuries: A Proof-of-Concept Study | journal = Frontiers in Surgery | volume = 7 | pages = 19 | date = 16 April 2020 | pmid = 32373625 | pmc = 7177010 | doi = 10.3389/fsurg.2020.00019 | doi-access = free }} tumors, and inflammation reduce anisotropy, as the barriers are affected by destruction or disorganization.

Anisotropy is measured in several ways. One way is by a ratio called fractional anisotropy (FA). An FA of 0 corresponds to a perfect sphere, whereas 1 is an ideal linear diffusion. Few regions have FA larger than 0.90. The number gives information about how aspherical the diffusion is but says nothing of the direction.

Each anisotropy is linked to an orientation of the predominant axis (predominant direction of the diffusion). Post-processing programs are able to extract this directional information.

This additional information is difficult to represent on 2D grey-scaled images. To overcome this problem, a color code is introduced. Basic colors can tell the observer how the fibers are oriented in a 3D coordinate system, this is termed an "anisotropic map". The software could encode the colors in this way:

• Red indicates directions in the X axis: right to left or left to right.
• Green indicates directions in the Y axis: posterior to anterior or from anterior to posterior.
• Blue indicates directions in the Z axis: inferior to superior or vice versa.

The technique is unable to discriminate the "positive" or "negative" direction in the same axis.

Using diffusion tensor MRI, one can measure the apparent diffusion coefficient at each voxel in the image, and after multilinear regression across multiple images, the whole diffusion tensor can be reconstructed.{{cite journal | vauthors = Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A | title = In vivo fiber tractography using DT-MRI data | journal = Magnetic Resonance in Medicine | volume = 44 | issue = 4 | pages = 625–632 | date = October 2000 | pmid = 11025519 | doi = 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O | doi-access = }}

Suppose there is a fiber tract of interest in the sample. Following the Frenet–Serret formulas, we can formulate the space-path of the fiber tract as a parameterized curve:

:$\backslash frac\{d\backslash mathbf\{r\}(s)\}\{ds\}\; =\; \backslash mathbf\{T\}(s),$

where $\backslash mathbf\{T\}(s)$ is the tangent vector of the curve. The reconstructed diffusion tensor $D$ can be treated as a matrix, and we can compute its eigenvalues $\backslash lambda\_1,\; \backslash lambda\_2,\; \backslash lambda\_3$ and eigenvectors $\backslash mathbf\{u\}\_1,\; \backslash mathbf\{u\}\_2,\; \backslash mathbf\{u\}\_3$. By equating the eigenvector corresponding to the largest eigenvalue with the direction of the curve:

:$\backslash frac\{d\backslash mathbf\{r\}(s)\}\{ds\}\; =\; \backslash mathbf\{u\}\_1(\backslash mathbf\{r\}(s))$

we can solve for $\backslash mathbf\{r\}(s)$ given the data for $\backslash mathbf\{u\}\_1(s)$. This can be done using numerical integration, e.g., using Runge–Kutta, and by interpolating the principal eigenvectors.

• Connectome
• Diffusion MRI
• Connectogram

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Category:Magnetic resonance imaging