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Visual Knowledge Discovery for Diffusion Kurtosis Datasets of the Human Brain

  • Sujal BistaEmail author
  • Jiachen Zhuo
  • Rao P. Gullapalli
  • Amitabh Varshney
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Classification and visualization of structures in the human brain provide vital information to physicians who examine patients suffering from brain diseases and injuries. In particular, this information is used to recommend treatment to prevent further degeneration of the brain. Diffusion kurtosis imaging (DKI) is a new magnetic resonance imaging technique that is rapidly gaining broad interest in the medical imaging community, due to its ability to provide intricate details on the underlying microstructural characteristics of the whole brain. DKI produces a fourth-order tensor at every voxel of the imaged volume; unlike traditional diffusion tensor imaging (DTI), DKI measures the non-Gaussian property of water diffusion in biological tissues. It has shown promising results in studies on changes in grey matter and mild traumatic brain injury, a particularly difficult form of TBI to diagnose. In this paper, we use DKI imaging and report our results of the classification and visualization of various tissue types, diseases, and injuries. We evaluate segmentation performed using various clustering algorithms on different segmentation strategies including fusion of diffusion and kurtosis tensors. We compare our result to the well-known MRI segmentation technique based on Magnetization-Prepared Rapid Acquisition with Gradient Echo (MPRAGE) imaging.

Keywords

Traumatic Brain Injury Fractional Anisotropy Diffusion Tensor Image Spherical Harmonic Gaussian Mixture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are grateful to the anonymous reviewers whose constructive comments have greatly improved the presentation of our approach and results in this paper. We appreciate the support of the U.S. Army grant W81XWH-12-1-0098, NSF grants 09-59979 and 14-29404, the State of Maryland’s MPower initiative, and the NVIDIA CUDA Center of Excellence. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the research sponsors.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sujal Bista
    • 1
    Email author
  • Jiachen Zhuo
    • 2
  • Rao P. Gullapalli
    • 2
  • Amitabh Varshney
    • 3
  1. 1.Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA
  2. 2.University of Maryland School of MedicineBaltimoreUSA
  3. 3.Department of Computer Science and Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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