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Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
Published on: May 7, 2019
Structural abnormalities of trigeminal root with neurovascular compression revealed by high resolution diffusion
Jing Chen1, Zi-Yi Guo, Qi-Zhou Liang
1Radiology Department, Municipal Hospital of Haikou, Hainan, China.
Asian Pacific Journal of Tropical Medicine
|July 19, 2012
Summary
Diffusion tensor imaging (DTI) reveals structural changes in trigeminal nerves (TGN) affected by neurovascular compression. These changes, including altered diffusion metrics and reduced nerve size, are key indicators for diagnosing trigeminal neuralgia (TN).
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Trigeminal neuralgia (TN) is often caused by neurovascular compression of the trigeminal nerve (TGN).
- Accurate detection of structural abnormalities in TGN is crucial for diagnosis and treatment planning.
Purpose of the Study:
- To utilize diffusion tensor imaging (DTI) for detecting structural abnormalities in trigeminal nerves (TGN) caused by neurovascular compression.
- To evaluate the diagnostic value of DTI in patients with trigeminal neuralgia (TN).
Main Methods:
- Diffusion tensor imaging (DTI) and 3D high-resolution MRI were performed on 20 TN patients and 10 controls.
- Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were measured for ipsilateral TGN (iTGN), contralateral TGN (cTGN), and normal TGN (nTGN).
Main Results:
- Affected iTGN showed significantly lower FA and smaller volume/cross-sectional area compared to cTGN and nTGN.
- Affected iTGN exhibited a significantly higher ADC compared to cTGN and nTGN.
- These findings indicate significant structural and diffusion changes in compressed TGN.
Conclusions:
- Increased ADC and decreased FA are closely related to the morphological changes of TGN in TN patients.
- DTI provides valuable diagnostic information regarding TGN structure in the context of TN.
- DTI can effectively detect neurovascular compression-induced abnormalities in TGN.

