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Related Concept Videos

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
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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 small or...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Published on: August 11, 2016

In vivo precision of bootstrap algorithms applied to diffusion tensor imaging data.

Robert S Vorburger1, Carolin Reischauer, Katerina Dikaiou

  • 1Institute for Biomedical Engineering, University and ETH Zurich, Switzerland. vorburger@biomed.ee.ethz.ch

Journal of Magnetic Resonance Imaging : JMRI
|June 13, 2012
PubMed
Summary
This summary is machine-generated.

Bootstrap algorithms precisely estimate diffusion tensor imaging measurement uncertainty. Model-based methods are recommended for clinical use due to their efficiency and high precision in diffusion parameter analysis.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biostatistics

Background:

  • Diffusion Tensor Imaging (DTI) is crucial for analyzing white matter microstructure.
  • Accurate estimation of measurement uncertainty in DTI-derived parameters is essential for reliable clinical applications.
  • Bootstrap algorithms offer a robust method for quantifying this uncertainty.

Purpose of the Study:

  • To evaluate the precision of model-based and non-model-based bootstrap algorithms for in vivo DTI measurement uncertainty estimation.
  • To compare the performance of different bootstrap methods across various brain regions.

Main Methods:

  • Four bootstrap algorithms were applied to DTI datasets from 10 repeated imaging sessions.
  • Measurement uncertainty was assessed in selected regions of interest (ROIs) and whole-brain white/gray matter.
  • Precision was quantified using coefficients of variation and intra-class correlation coefficients, with simulations for validation.

Main Results:

  • All bootstrap methods demonstrated comparable precision, with minor variations based on ROIs.
  • Average coefficients of variation were 13.81% (ADC), 12.35% (FA), and 17.93% (CoU).
  • High intra-class correlation coefficients (>0.96) indicated excellent reproducibility; simulations supported in vivo findings.

Conclusions:

  • Investigated bootstrap methods provide similar, high precision for diffusion parameter measurement uncertainty.
  • Time-efficient, model-based bootstrap approaches are recommended for clinical practice due to their performance and efficiency.