Analyzing Brain Microstructure Using Diffusion Weighted Imaging in Python
DOI:
https://doi.org/10.32628/IJSRCSEITAbstract
Diffusion Weighted Imaging (DWI) is a sophisticated MRI technique that enables the visualization of water molecule movement within biological tissues, offering critical insights into the brain's micro-structural integrity. This project focuses on implementing a Python based pipeline to analyze DWI data with the aim of detecting and classifying abnormalities such as strokes, tumors, and neuro degenerative diseases. Through data pre- processing, diffusion metric calculation (e.g., Fractional Anisotropy and Mean Diffusivity), and machine learni ng algorithms, the project facilitates accurate classification and visualization of brain regions. The use of open- source Python libraries and visualization tools allows for the creation of a robust, automated,and scalable framework for both clinical and research applications. The results provide improved diagnostic capabilities and a deeper understanding of brain connectivity, laying a foundation for further advancements in computational neuro imaging.
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References
Smith, S. M., & Nichols, T. E. (2009). Diffusion Tensor Imaging: The Basics. NeuroImage, 45(1), S189– S199. https://doi.org/10.1016/j.neuroimage.2008.10.001
Zhang, H., Yushkevich, P. A., Alexander, D. C., & Gee, J. C. (2018). Automated Stroke Segmentation Using DWI. IEEE Transactions on Medical Imaging, 37(9), 2149–2160. https://doi.org/10.1109/TMI.2018.2821244
Tournier, J. D., Mori, S., & Leemans, A. (2011). Diffusion Tensor Imaging and Beyond. Magnetic Resonance in Medicine, 65(6), 1532–1556. https://doi.org/10.1002/mrm.22924
Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1412.6980
Avants, B. B., Tustison, N. J., & Song, G. (2011). A Comprehensive Framework for Diffusion MRI Analysis. Insight Journal. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3071855/
Jones, D. K. (2011). Diffusion MRI: Theory, Methods, and Applications. Oxford University Press.https://global.oup.com/academic/product/diffusion-mri-9780199708709
Haacke, E. M., Brown, R. W., Thompson, M. R., & Venkatesan, R. (1999). Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley-Liss. https://www.wiley.com/en- in/Magnetic+Resonance+Imaging%3A+Physical+Princip les+and+Sequence+Design%2C+2nd+Edition-p-9780471351283
Gonzalez, R. C., & Woods, R. E. (2017). Digital Image Processing (4th ed.). Pearson Education. https://www.pearson.com/en-us/subject-catalog/p/digital- image-processing/P200000003938
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://link.springer.com/book/10.1007/978-0-387-45528-0
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
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