ADVANCED NEUROLOGICAL IMAGING ANALYSIS USING DIFFUSION TENSOR TECHNIQUES AND DISTRIBUTED WEB SYSTEMS
DOI:
https://doi.org/10.59277/RRST-EE.2025.2.20Keywords:
Diffusion weighted imaging, Diffusion tensor imaging, Neurological diagnosis, Medical image analysis, Fractional anisotropy, Mean diffusivity, Distributed web systems, FSL software library, MRI image processing, Medical data sharing, Scalable computing, Brain connectivity analysisAbstract
Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are crucial in modern neurological diagnostics, enabling detailed analysis of brain structures and connectivity. This article presents a comprehensive approach to analysing MRI images using advanced tools such as the FSL software library. The proposed method leverages distributed web systems to enhance the scalability and accessibility of image processing and analysis across multiple medical facilities. Key steps, including noise reduction, artefact removal, and tensor reconstruction, are performed to improve diagnostic accuracy. Additionally, metrics such as fractional anisotropy (FA), mean diffusivity (MD), and axial diffusivity (AD) are evaluated to detect microstructural brain abnormalities. The integration of distributed web technologies facilitates real-time collaboration between specialists, accelerating diagnostic processes and enabling cross-hospital data sharing. This study highlights the potential of combining cutting-edge imaging techniques with scalable digital infrastructures to optimise medical decision-making and improve patient outcomes.
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