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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Longitudinal brain MRI analysis with uncertain registration.

Ivor J A Simpson1, Mark W Woolrich, Adrian R Groves

  • 1Institute of Biomedical Engineering, University of Oxford, Oxford. ivor.simpson@eng.ox.ac.uk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 15, 2011
PubMed
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This study introduces a new method for spatial uncertainty in brain image analysis, improving Alzheimer's disease classification accuracy. The approach enhances statistical analysis by adaptively smoothing data based on registration accuracy.

Area of Science:

  • Medical Imaging
  • Neuroimaging Analysis
  • Biostatistics

Background:

  • Current spatial normalization methods in statistical analysis rely on point estimates, which are often inaccurate.
  • This inaccuracy necessitates data smoothing to compensate for registration uncertainties, potentially obscuring subtle findings.

Purpose of the Study:

  • To develop a novel approach for incorporating spatial uncertainty measures into spatially normalized statistics.
  • To improve the accuracy of statistical analysis in neuroimaging by accounting for registration inaccuracies.

Main Methods:

  • Derived localized measurements of spatial uncertainty from a probabilistic registration framework.
  • Applied this framework to longitudinal deformation features from MR brain images (Alzheimer's Disease Neuroimaging Initiative).

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Published on: September 25, 2019

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  • Spatially normalized images using a probabilistic registration algorithm and adaptively smoothed features based on registration uncertainty.

    • Main Results:

    • The proposed adaptive smoothing method improved classification of Alzheimer's Disease versus controls to 84% accuracy.
    • This outperformed analyses with no smoothing (79.6%) and standard Gaussian smoothing (78.8%).

    Conclusions:

    • Incorporating spatial uncertainty from non-rigid registration into statistical analysis provides a principled approach to image smoothing.
    • Adaptive smoothing based on registration uncertainty enhances classification performance in neuroimaging studies, particularly for conditions like Alzheimer's disease.