Figure 1 MRI and DTI

Brain activity may be visualised in great detail using MRI and functional MRI. It is employed to pinpoint precisely which region of the brain controls important processes including thought, speech, vision, movement, and sensation. Additionally, it can display how Alzheimer’s disease, trauma, or a stroke affects brain function. Using a technique called Diffusion Tensor Imaging (DTI), it is possible to track the path that water takes through the brain’s white matter pathways. The brain’s white-matter pathways, which connect various regions, act like a safeguarded during surgery. The white matter fibres that link various areas of the brain are found using DTI. Despite the fact that traumatic brain injury has been the focus of most DTI research, it also has other uses, such as helping to diagnose, predict outcomes, categorise stroke, brain tumours, neurodegenerative diseases, developmental disorders, neuropsychiatric disorders, movement disorders, and neurogenetic developmental disorders. Numerous measurements that are computed using DTI can offer quantitative power. Fractional anisotropy (FA) is one of the DTI parameters that are most frequently utilized for Brain tumour diagnosis; others are Radial Diffusivity (perpendicular), Axial Diffusivity (parallel), Average Diffusivity and Apparent Diffusion Coefficient (ADC). FA is highly sensitive to changes in microstructure and as a summary quantifies the directionality of diffusivity, but it is not always able to pinpoint the exact origin of a change. The region of interest (ROI) approach, whole-brain analysis (Voxel-Based analysis), and tract-based spatial statistics can all be used to compute FA values. Due to its automation and capacity to examine more tracts, whole-brain analysis is becoming more and more popular. The ROI approach, in which the regions to be examined are marked by a technician before being examined by a computer, is still dependable and repeatable.

References:

·         Drevelegas, A. (2002). Imaging Modalities in Brain Tumors. In: Drevelegas, A. (eds) Imaging of Brain Tumors with Histological Correlations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04951-8_2

·         Ranzenberger LR, Snyder T. Diffusion Tensor Imaging. [Updated 2022 Jul 26]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

·         Yahya M A Mohammed and others, A survey of methods for brain tumor segmentation-based MRI images, Journal of Computational Design and Engineering, Volume 10, Issue 1, February 2023, Pages 266–293, https://doi.org/10.1093/jcde/qwac141.

·         Işın, Ali & Direkoglu, Cem & Sah, Melike. (2016). Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods. Procedia Computer Science. 102. 317-324. 10.1016/j.procs.2016.09.407.