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Quantifying Mixing using Magnetic Resonance Imaging
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Bayesian uncertainty quantification for magnetic resonance fingerprinting.

Selma Metzner1, Gerd Wübbeler1, Sebastian Flassbeck2,3

  • 1Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.

Physics in Medicine and Biology
|March 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for uncertainty quantification in Magnetic Resonance Fingerprinting (MRF), enabling reliable probability distributions for T1 and T2 relaxation times. This advance allows for accurate assessment of tissue changes using fast quantitative MRI.

Keywords:
Bayesian inferenceMRFuncertainty

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Area of Science:

  • Medical Imaging
  • Quantitative MRI
  • Biophysics

Background:

  • Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative imaging of human tissues, providing T1 and T2 relaxation times.
  • Current dictionary-based MRF methods lack robust uncertainty characterization, hindering the assessment of estimate significance and pathological changes.

Purpose of the Study:

  • To develop and validate a Bayesian approach for uncertainty quantification in dictionary-based MRF.
  • To enable probability statements and assign uncertainties to voxel-wise T1 and T2 MR relaxation time estimates.

Main Methods:

  • A Bayesian framework was applied to MRF data, utilizing pre-computed dictionaries and undersampled MR images.
  • The approach generates probability distributions for T1 and T2 relaxation times in each voxel.
  • Uncertainty calculations were performed rapidly, based on the existing MRF data and dictionary.

Main Results:

  • The proposed method quantitatively aligns with observed variability in phantom MRF measurements.
  • Calculated uncertainties effectively characterize differences between MRF estimates and high-accuracy reference measurements.
  • Successful application demonstrated on simulated data and an in vivo MRF measurement.

Conclusions:

  • The developed Bayesian approach provides reliable uncertainty quantification for dictionary-based MRF.
  • This enables more confident interpretation of T1 and T2 relaxation time estimates for clinical applications.
  • The method offers a significant advancement for assessing tissue properties and potential pathologies using fast quantitative MRI.