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Robust phase unwrapping for MR temperature imaging using a magnitude-sorted list, multi-clustering algorithm.

Florian Maier1, David Fuentes, Jeffrey S Weinberg

  • 1Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

Magnetic Resonance in Medicine
|May 10, 2014
PubMed
Summary
This summary is machine-generated.

A new automatic phase unwrapping algorithm was developed for MR temperature imaging. It robustly handles clinical data and achieves fast processing times for real-time temperature map updates.

Keywords:
MRImulti-clusteringphase unwrappingsorted listthermometry

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Imaging
  • Biophysics

Background:

  • Phase information in MRI is crucial for various applications like B0 field mapping and chemical shift imaging.
  • Accurate phase unwrapping is essential for reliable quantitative MRI measurements.
  • Existing phase unwrapping methods may not be optimized for the specific demands of MR temperature imaging.

Purpose of the Study:

  • To develop a fully automatic phase unwrapping algorithm tailored for MR temperature imaging.
  • To ensure robustness and efficiency for clinical applications.
  • To enable real-time temperature mapping.

Main Methods:

  • A novel algorithm sorts pixels by magnitude and iteratively creates and merges unwrapped pixel clusters.
  • The method performs fully automatic phase unwrapping.
  • Evaluation involved simulated phantom and clinical in vivo MR temperature imaging data.

Main Results:

  • The algorithm showed no errors in phantom data with signal-to-noise ratios (SNR) >= 4.5.
  • In vivo data analysis revealed minimal failure (avg. <1 pixel) for SNR > 6.3.
  • Processing times were under 30 ms per image for typical regions of interest.

Conclusions:

  • The developed phase unwrapping algorithm demonstrates robust performance with clinical in vivo MR temperature imaging data.
  • Fast processing times facilitate online temperature map updates without perceptible delay.
  • This advancement supports real-time monitoring in MR-guided thermal therapies.