Diffusion Tensor Imaging Unboxing
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Diffusion Tensor Imaging Unboxing

A quick guide for beginner researchers

About the Book

This guide is designed for researchers who still do not have or have little contact with the diffusion weighted imaging modality (DTI) in MRI. After a few decades of research and development, is now known that the DTI images are extremely powerful for several uses in clinical routine. However, along with these advances of this imaging modality, there are several computational tools for image reconstruction and its visualization. Do not be concerned about what is the first step to understand the DTI imaging modality, because this guide have the intention to summarize the main tools and a brief discussion of what is important to DTI image for modern medicine. See in more detail in the table of contents of this book below or simply download a sample for free.

About the Author

Antonio Senra Filho
Antonio Senra Filho

Antonio Senra Filho has been intensively focused on intelligent systems and how to build machine learning approaches for real-case scenarios. With several known scientific research activities in journals and international conferences, the outcomes reported in the past years helped to achieve a better understanding of practical solutions that can be applied to commercial issues, e.g. document analysis, industry 4.0, smart cities, and so on. Therefore, there is a natural intersection between high-dimensional image analysis, image filtering, multidimensional feature extraction, artificial intelligence, and deep learning algorithms.

Antonio prides himself on sharing his knowledge and experience so feel free to contact him if you have questions, comments or need assistance. LinkedIn and other social network profiles are found by @acsenrafilho.

Table of Contents

    • 1. Preface
    • 2. How to use this book
      • 2.1 Tips for additional reading
      • 2.2 Additional markers
    • 3. Why DTI matters?
      • 3.1 DTI clinical application
      • 3.2 Diffusion imaging technique advances
      • 3.3 References
    • 4. Principles of MRI-DTI
      • 4.1 A little of MRI physics
        • 4.1.1 Diffusion Weighted Imaging (DWI)
        • 4.1.2 Diffusion Tensor Imaging (DTI)
      • 4.2 References
    • 5. DTI data reconstruction pipeline
      • 5.1 Image pre-processing
        • 5.1.1 Data conversion
        • 5.1.2 Edge current correction
        • 5.1.3 Brain extraction
        • 5.1.4 Noise attenuation
        • 5.1.5 Registration
      • 5.2 Quantitative and qualitative outputs
        • 5.2.1 Reconstruted maps
        • 5.2.2 Tractography
      • 5.3 References
    • 6. Useful Tools
      • 6.1 FSL
        • 6.1.1 TBSS
        • 6.1.2 BEDPOSTX and PROBTRACKX
      • 6.2 3D Slicer
      • 6.3 MedINRIA
      • 6.4 ExploreDTI
      • 6.5 DSI Studio
      • 6.6 DTI-TK
        • 6.6.1 Tract-Specific Analysis (TSA)
      • 6.7 References
    • 7. Novelties and trends
      • 7.1 HARDI and DSI
      • 7.2 Ultra high field MRI
      • 7.3 Image processing techniques
        • 7.3.1 Noise attenuation
        • 7.3.2 Anomalous diffusion filters
        • 7.3.3 Non Local Means applied in DTI
        • 7.3.4 LMMSE
      • 7.4 Reconstruction paradigms
        • 7.4.1 Anomalous relaxation
      • 7.5 Image databases
      • 7.6 References
  • Notes

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