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SENSE phase-constrained magnitude reconstruction with iterative phase refinement.

Calvin Lew1, Angel R Pineda, David Clayton

  • 1Lucas MRS/I Center, Stanford University, Stanford, California 94035, USA. cdlew@stanfordalumni.org

Magnetic Resonance in Medicine
|October 31, 2007
PubMed
Summary

This study introduces an improved phase-refinement method for Magnetic Resonance Imaging (MRI) reconstruction. This technique enhances image quality by reducing noise and artifacts, especially at high acceleration factors.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Signal Processing

Background:

  • Conventional Sensitivity Encoding (SENSE) reconstruction uses complex-valued equations, but many MRI applications only require magnitude data.
  • Existing phase-constrained SENSE methods suffer from aliasing artifacts due to poor phase estimates and sensitivity to phase errors.

Purpose of the Study:

  • To develop an iterative phase-refinement method for SENSE reconstruction to improve image quality.
  • To reduce g-factor-related noise enhancement and aliasing artifacts in MRI.
  • To achieve better noise performance and SNR improvement, particularly for magnitude-only MRI applications.

Main Methods:

  • An iterative scheme was developed to refine phase estimates for SENSE reconstruction.
  • The mathematical framework for the new phase-refinement approach was established.
  • Comparisons were made between conventional SENSE, phase-constrained SENSE, and the new phase-refinement method.

Main Results:

  • The new phase-refinement method demonstrated significantly better noise performance at high reduction factors compared to conventional SENSE.
  • Experimental verification confirmed superior noise properties and reduced aliasing artifacts.
  • The method offers substantial SNR improvement and fewer artifacts for magnitude-only MRI applications.

Conclusions:

  • The iterative phase-refinement method provides a significant advancement over conventional and previous phase-constrained SENSE techniques.
  • This approach is particularly beneficial for MRI applications requiring only magnitude information.
  • The method achieves near-theoretical noise performance limits at high acceleration factors.