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diffusion_tensor_imaging_tractography

Diffusion Tensor Imaging Tractography

see Intraoperative Diffusion Tensor Imaging Tractography.

Since its introduction, diffusion tensor imaging (DTI) has become an important tool in neuroscience given its unprecedented ability to image brain white matter in vivo.

It is commonly used in neurosurgical practice but is largely limited to the preoperative setting.

The interest in understanding the mechanisms of action of Deep Brain Stimulation in different targets and indications, together with the constant drive towards the improvement in long-term clinical outcomes, has found a logical complement in the application of tractography in this field. Diffusion tensor imaging has been traditionally associated with an increased susceptibility to MRI artifacts, and expensive computational resources. Recent advances have however improved these restrictions, allowing for countless applications in Neurosurgery, as demonstrated by the large number of original research papers published in the last decade 1).

Diffusion tensor imaging (DTI) for the assessment of fractional anisotropy (FA) and involving measurements of mean diffusivity (MD) and apparent diffusion coefficient (ADC) represents a, MRI-based, noninvasive technique that may delineate microstructural changes in cerebral white matter (WM).

Diffusion tensor imaging (DTI) provides a measure of the directional diffusion of water molecules in tissues and can noninvasively detect in vivo white matter (WM) abnormalities on the basis of anisotropic diffusion properties.

From the diffusion tensor, the principal direction of the diffusion tensor can be used to infer the white-matter connectivity of the brain (i.e. tractography; trying to see which part of the brain is connected to which other part).

see Neuronavigation with diffusion tensor imaging.

Indications

Limitations

DTI, has limitations that include its inability to resolve multiple crossing fibers and its susceptibility to partial volume effects.

Therefore, recent focus has shifted to more advanced WM imaging techniques such as high-definition fiber tractography (HDFT).

Abhinav et al. illustrate the application of HDFT, which in their preliminary experience has enabled accurate depiction of perilesional tracts in a 3-dimensional manner in multiple anatomical compartments including edematous zones around high-grade gliomas. This has facilitated accurate surgical planning. This is illustrated by using case examples of patients with glioblastoma multiforme. They also discuss future directions in the role of these techniques in surgery for gliomas 2).


Diffusion tensor imaging tractography is the mapping of neural fiber pathways based on diffusion tensor imaging (DTI) of tissue diffusion anisotropy.

It has become a popular tool for delineating white matter tracts for neurosurgical procedures.

It uses special techniques of magnetic resonance imaging (MRI), and computer-based image analysis. The results are presented in two- and three-dimensional images.

In addition to the long tracts that connect the brain to the rest of the body, there are complicated neural networks formed by short connections among different cortical and subcortical regions. The existence of these bundles has been revealed by histochemistry and biological techniques on post-mortem specimens. Brain tracts are not identifiable by direct exam, CT, or MRI scans. This difficulty explains the paucity of their description in neuroanatomy atlases and the poor understanding of their functions.

The MRI sequences used look at the symmetry of brain water diffusion. Bundles of fiber tracts make the water diffuse asymmetrically in a tensor, the major axis parallel to the direction of the fibers. The asymmetry here is called anisotropy. There is a direct relationship between the number of fibers and the degree of anisotropy.


White matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to region of interest or fractional anisotropy values.

An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function.

The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest.

Tunç et al., report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections 3).

1)
Sammartino F, Hodaie M. Diffusion Tensor Imaging of the Basal Ganglia for Functional Neurosurgery Applications. Prog Neurol Surg. 2018;33:62-79. doi: 10.1159/000480766. Epub 2018 Jan 12. PubMed PMID: 29332074.
2)
Abhinav K, Yeh FC, Mansouri A, Zadeh G, Fernandez-Miranda JC. High-definition fiber tractography for the evaluation of perilesional white matter tracts in high-grade glioma surgery. Neuro Oncol. 2015 Jun 27. pii: nov113. [Epub ahead of print] Review. PubMed PMID: 26117712.
3)
Tunç B, Ingalhalikar M, Parker D, Lecoeur J, Singh N, Wolf RL, Macyszyn L, Brem S, Verma R. Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning. Neurosurgery. 2016 Oct;79(4):568-77. doi: 10.1227/NEU.0000000000001183. PubMed PMID: 26678299; PubMed Central PMCID: PMC4911597.
diffusion_tensor_imaging_tractography.txt · Last modified: 2019/08/18 21:00 by administrador