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Visual Knowledge Discovery for Diffusion Kurtosis Datasets of the Human Brain

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Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Part of the book series: Mathematics and Visualization ((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.

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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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Authors and Affiliations

  1. Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 20742, USA

    Sujal Bista

  2. University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    Jiachen Zhuo & Rao P. Gullapalli

  3. Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 20742, USA

    Amitabh Varshney

Authors
  1. Sujal Bista
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  2. Jiachen Zhuo
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  3. Rao P. Gullapalli
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  4. Amitabh Varshney
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Corresponding author

Correspondence to Sujal Bista .

Editor information

Editors and Affiliations

  1. Linkö** University, Norrkö**, Sweden

    Ingrid Hotz

  2. Visualization and Medical Image Analysis, University of Bonn, Bonn, Germany

    Thomas Schultz

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Bista, S., Zhuo, J., Gullapalli, R.P., Varshney, A. (2015). Visual Knowledge Discovery for Diffusion Kurtosis Datasets of the Human Brain. In: Hotz, I., Schultz, T. (eds) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-15090-1_11

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