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General phase regularized reconstruction using phase cycling.

Frank Ong1, Joseph Y Cheng2, Michael Lustig1

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA.

Magnetic Resonance in Medicine
|November 22, 2017
PubMed
Summary
This summary is machine-generated.

A new phase cycling reconstruction method improves MRI image quality by reducing artifacts in partial Fourier, water-fat, and flow imaging. This technique offers robust joint reconstruction without phase unwrapping.

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Area of Science:

  • Magnetic Resonance Imaging
  • Medical Image Reconstruction
  • Computational Imaging

Background:

  • Phase constraints are crucial for accurate MRI reconstruction, particularly in advanced techniques like partial Fourier imaging, water-fat imaging, and flow imaging.
  • Existing reconstruction methods can be sensitive to phase wraps and may not effectively handle joint reconstruction tasks.
  • Regularized inverse problem frameworks are essential for addressing the complexities of MRI data reconstruction.

Purpose of the Study:

  • To develop a generalized phase regularized image reconstruction method.
  • To enable joint reconstruction for multiple MRI applications, including partial Fourier, water-fat, and flow imaging.
  • To introduce a novel phase cycling technique to overcome limitations of existing phase-regularized methods.

Main Methods:

  • A phase regularized reconstruction algorithm was developed within an inverse problem framework.
  • A phase cycling technique was introduced to ensure invariance to phase wraps, addressing non-convexity issues.
  • The proposed method was applied to retrospectively undersampled in vivo datasets and compared with state-of-the-art techniques.

Main Results:

  • Phase cycling reconstructions demonstrated a significant reduction in artifacts compared to methods without phase cycling.
  • The proposed method achieved performance comparable to state-of-the-art results for partial Fourier, water-fat, and flow imaging reconstruction.
  • Successful joint reconstruction was demonstrated for combined partial Fourier, water-fat imaging, parallel imaging (PI), and compressed sensing (CS), as well as partial Fourier, divergence-free regularized flow imaging, PI, and CS.

Conclusions:

  • The phase cycling reconstruction method provides an effective alternative for phase-regularized MRI reconstruction.
  • The technique eliminates the need for phase unwrapping and is robust to initial solution choices.
  • This approach facilitates the joint reconstruction of various phase imaging applications in MRI.