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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Cortical Morphometry Analysis based on Worst Transportation Theory.

Min Zhang1, Dongsheng An2, Na Lei3

  • 1Zhejiang University.

Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 22, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel biomarker using worst transportation cost to track Alzheimer's disease (AD) progression by analyzing cortical morphometry. This new method shows improved statistical performance over existing techniques for early AD detection.

Keywords:
Alzheimer’s diseaseShape analysisWorst transportation

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Area of Science:

  • Neuroscience
  • Computational Geometry
  • Medical Imaging

Background:

  • Biomarkers are crucial for early Alzheimer's disease (AD) detection and intervention.
  • Identifying effective AD biomarkers remains a significant challenge in clinical research.

Purpose of the Study:

  • To introduce the worst transportation (WT) cost as a novel univariate biomarker for indexing cortical morphometry.
  • To track Alzheimer's disease progression using WT cost analysis.
  • To compare the efficacy of WT cost against optimal transportation (OT) and other methods.

Main Methods:

  • Generalized Brenier theorem for the worst transportation map, showing it's a gradient of a concave function satisfying the Monge-Ampere equation.
  • Developed an efficient computational geometry algorithm to compute the WT map.
  • Applied the algorithm to analyze cortical shape differences in individuals with AD dementia versus normal aging.

Main Results:

  • The proposed worst transportation cost method effectively indexes cortical morphometry for AD progression.
  • Experimental results demonstrate superior statistical performance compared to optimal transportation and other competing methods.
  • The method successfully identified cortical shape differences between AD dementia and normal aging.

Conclusions:

  • Worst transportation cost serves as a promising univariate biomarker for tracking Alzheimer's disease progression.
  • The developed WT algorithm provides an efficient and effective tool for neuroimaging analysis in AD research.
  • This approach offers a potential advancement in early AD detection and intervention strategies.