A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts

Chaoqing Xu, Tyson Allan Neuroth, Takanori Fujiwara, Ronghua Liang, Kwan Liu Ma

Research output: Contribution to journalArticlepeer-review

Abstract

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.

Original languageEnglish (US)
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
StateAccepted/In press - 2021

Keywords

  • Brain fiber tracts
  • Data visualization
  • Diffusion tensor imaging
  • Diseases
  • Feature extraction
  • machine learning
  • neurodegenerative disease
  • predictive visual analytics
  • Rendering (computer graphics)
  • Tensors
  • Visual analytics
  • visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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