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Related Concept Videos

Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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A Generalized Linear modeling approach to bootstrapping multi-frame PET image data.

Finbarr O'Sullivan1, Fengyun Gu1, Qi Wu1

  • 1Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, T12XF62, Ireland.

Medical Image Analysis
|June 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a practical image-domain bootstrap method for Positron Emission Tomography (PET) imaging, enabling uncertainty evaluation in dynamic studies. This approach provides reliable kinetic parameter uncertainty maps, crucial for cancer patient management.

Keywords:
BootstrapGaussian processGeneralized linear modelsImage analysisKinetics and residuesNon-parametric methodsPETSampling variationSpectral analysis

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Statistical Modeling

Background:

  • Positron Emission Tomography (PET) is vital for diagnosing cancer and other diseases.
  • Quantitative PET data requires uncertainty evaluation for improved medical decision-making.
  • Direct bootstrapping of raw PET data is computationally intensive, especially for dynamic or iterative reconstructions.

Purpose of the Study:

  • To develop a flexible statistical linear model for image-domain bootstrapping of dynamic PET data.
  • To enable the estimation of uncertainties in kinetic parameters derived from PET studies.
  • To provide a computationally practical alternative to projection-domain bootstrapping.

Main Methods:

  • A statistical linear model was applied to multi-frame PET image data to generate bootstrap samples.
  • The method was validated using dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients.
  • Quantitative performance was assessed via simulation, comparing image-domain to projection-domain bootstrapping.

Main Results:

  • The developed image-domain bootstrap method successfully generated valid bootstrap samples from archived PET image data.
  • Maps of kinetic parameters and their associated uncertainties were obtained, even without raw list-mode data.
  • The image-domain bootstrap demonstrated substantial agreement with the computationally impractical projection-domain alternative.
  • A strong correlation was observed between voxel-level kinetic parameter uncertainty and local reconstruction error.

Conclusions:

  • The proposed image-domain bootstrap method offers a computationally feasible approach for quantifying uncertainties in dynamic PET studies.
  • This technique is valuable for medical decisions based on quantitative PET imaging, particularly when raw data is unavailable.
  • The findings highlight the link between reconstruction quality and parameter uncertainty, aligning with statistical theory.