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MR fingerprinting reconstruction with Kalman filter.

Xiaodi Zhang1, Zechen Zhou2, Shiyang Chen3

  • 1The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, Suite W200, Atlanta, GA 30322, USA; Center for Biomedical Imaging Research, Tsinghua University, Beijing 100084, China.

Magnetic Resonance Imaging
|April 24, 2017
PubMed
Summary

Magnetic resonance fingerprinting (MRF) can now be reconstructed using a Kalman filter, eliminating the need for large dictionaries. This new method provides continuous MR parameter measurements, improving efficiency and accuracy in quantitative imaging.

Keywords:
Bloch equationDictionary matchingKalman filterMR fingerprinting

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Area of Science:

  • Medical Imaging
  • Quantitative MRI
  • Biophysics

Background:

  • Magnetic resonance fingerprinting (MRF) is a quantitative MRI technique for efficient multi-parameter mapping.
  • Current MRF reconstruction relies on dictionary matching, which has limitations due to discrete dictionaries and high computational costs.

Purpose of the Study:

  • To introduce and evaluate a novel Kalman filter-based reconstruction method for MRF.
  • To overcome the limitations of dictionary matching in MRF by enabling continuous parameter estimation.

Main Methods:

  • Derived the Bloch equations for the inversion-recovery balanced steady-state free-precession (IR-bSSFP) MRF sequence within a Kalman filter framework.
  • Used the Kalman filter to recursively predict signal evolution and update estimates with acquired MRF data.
  • Validated the Kalman filter algorithm using single-pixel and numerical brain phantom simulations.

Main Results:

  • The Kalman filter algorithm successfully reconstructed MR parameters without requiring a pre-defined dictionary.
  • Continuous MR parameter measurements were obtained, contrasting with the discrete nature of dictionary matching.
  • Simulations demonstrated the feasibility and performance of the Kalman filter for MRF reconstruction.

Conclusions:

  • The Kalman filter offers a viable and efficient alternative to dictionary matching for MRF reconstruction.
  • This method eliminates the need for dictionary pre-computation and storage, reducing computational burden.
  • The Kalman filter enables continuous MR parameter mapping, advancing quantitative magnetic resonance imaging.