MRI IN GLAUCOMA (PART 3)

Diffusion Kurtosis Imaging (DKI):

It is an advanced MRI technique that goes beyond standard diffusion imaging (DWI/DTI) by analyzing non-Gaussian water movement, revealing more complex tissue microstructure details in the brain and body, helping differentiate tissues, assess stroke severity, and detect subtle changes in conditions like Parkinson's or cancers, by measuring "kurtosis" which quantifies deviations from normal water diffusion due to cellular obstacles. Kurtosis (K) is a key parameter that quantifies this deviation, indicating tissue complexity and restriction.

Unlike DTI (Diffusion Tensor Imaging) which assumes simple, linear water movement, DKI captures how water diffusion deviates from a straight line (non-Gaussian) in complex tissues like the brain. DKI also provides additional advanced metrics like Mean Kurtosis (MK), Axial Kurtosis (AK), and Radial Kurtosis (RK), offering richer data than just diffusivity (MD, FA) provided by DTI. The RK reflects the complexity of fiber orientation and vertical orientation.

The DKI method is based on the principle that water diffusion in vivo moves in intercellular space and cells, and it does not have free motion. Therefore, the motion displacement of diffusion of real water molecules follows a non-Gaussian distribution. The greater that the diffusion of water molecules is limited by the surrounding environment, the more mixed the tissue components are in voxels, and the more significant the non-Gaussian nature of diffusion. 

Jensen et al. proposed the DKI model in 2005, which can be used to quantify the integrity of microstructure and tissue complexity even in the presence of cross fibers. 

Studies using DKI have shown that MK decreases along the brain visual pathway in patients with glaucoma. Xu et al. used 3T DKI to evaluate the damage to the microstructure of the visual pathway in patients with primary glaucoma. They found that glaucoma is a complex nervous system disease that affects the whole visual pathway. The RK of optic tract in glaucoma patients was significantly lower than that in healthy controls. This indicates that water diffusion perpendicular to the axon axis is less limited, which is consistent with the high RD in DTI.

Sun et al. found that in addition to the damage of axon integrity, glial cells were involved in glaucoma.

Studies have shown that NTG leads to the reorganization of information flow between the visual cortex and other brain regions, which is consistent with damage to the brain’s microstructure.

Magnetic Resonance Spectroscopy (MRS): 

The MRS is a non-invasive technology, which can detect, identify, and quantify biochemical compounds or metabolites in brain tissue. Due to the chemical heterogeneity of the human brain, there are various concentrations of important metabolites in normal brain structural areas, which provide different physiological and chemical information. Metabolites that can be measured using MRS include n-acetylaspartic acid (NAA), choline (Cho), creatine (Cr), lactate, glutamine, glutamate, lipids, and macromolecules. 

MRS creates detailed anatomical images (pictures) of the brain using water protons. MRS focuses on the chemical signals (frequencies) from specific brain chemicals within a defined spot (voxel). It generates a spectrum (graph) showing the relative amounts of different metabolites, indicating cellular activity and disease presence. While MRI shows where problems are, MRS shows what they're made of, revealing biochemical changes. 

Barbosa found that molecular biomarkers can help diagnose glaucoma and may lead to new targeted therapy.

MRS has revealed neurodegeneration of the LGB and the visual cortex in patients with glaucoma. These findings suggest that mapping the metabolic changes of the whole visual pathway by MRS will guide the treatment and follow-up plans of glaucoma patients.

There are some limitations to MRS: Quantification of a single metabolite is usually not feasible due to the presence of a large number of detectable metabolites. Therefore, although they have high diagnostic value, these methods make it more difficult to connect the obtained data with potential metabolic pathways.