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Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI.

Xuan Gu1,2, Anders Eklund1,2,3, Evren Özarslan1,2

  • 1Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.

Frontiers in Neuroinformatics
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

Quantifying uncertainty in multi-contrast parametric mapping magnetic resonance imaging (MAP-MRI) metrics is crucial. Using the wild bootstrap method reveals how acquisition schemes impact metric uncertainty, improving data analysis and interpretation.

Keywords:
MAP-MRINGPARTOPbootstrapdiffusion MRIuncertainty

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Area of Science:

  • Neuroimaging
  • Diffusion MRI
  • Quantitative MRI

Background:

  • Multi-contrast parametric mapping magnetic resonance imaging (MAP-MRI) provides advanced metrics for characterizing tissue microstructure.
  • Estimating the uncertainty of these MAP-MRI metrics is essential for robust interpretation and reliable application in research and clinical settings.
  • Bootstrap methods, such as the wild bootstrap, offer a powerful approach for quantifying uncertainty in complex imaging metrics.

Purpose of the Study:

  • To quantify the uncertainty of key MAP-MRI metrics, including return-to-origin probability (RTOP), non-Gaussianity (NG), and propagator anisotropy (PA).
  • To investigate the influence of diffusion acquisition schemes (number of shells and measurements per shell) on the uncertainty of MAP-MRI metrics.
  • To demonstrate the utility of incorporating uncertainty estimates in group analyses and for comparing different preprocessing pipelines in diffusion MRI studies.

Main Methods:

  • Employed the wild bootstrap technique to estimate empirical distributions and quantify the uncertainty of MAP-MRI metrics.
  • Applied the wild bootstrap method to both phantom and human brain diffusion MRI datasets.
  • Calculated uncertainty for return-to-origin probability (RTOP), non-Gaussianity (NG), and propagator anisotropy (PA).

Main Results:

  • Demonstrated the significant impact of diffusion acquisition parameters (number of shells, measurements per shell) on the uncertainty of MAP-MRI metrics.
  • Showcased how considering metric uncertainty can enhance group analyses, leading to potentially different conclusions compared to analyses without uncertainty estimation.
  • Illustrated the value of uncertainty quantification in comparing the performance of different diffusion MRI preprocessing pipelines.

Conclusions:

  • Bootstrap-derived uncertainty measures provide valuable complementary information to standard MAP-MRI metrics.
  • Incorporating these uncertainty measures is recommended for ongoing and future MAP-MRI studies to enhance insight and improve analytical rigor.
  • Uncertainty quantification facilitates more reliable comparisons across different acquisition protocols and preprocessing strategies.