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A dual-interpolator method for improving parallel MRI reconstruction.

Yuchou Chang1, Huy Anh Pham2, Zhiqiang Li3

  • 1Department of Computer and Information Science Department, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA.

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|June 30, 2022
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Summary
This summary is machine-generated.

This study introduces a dual-interpolator strategy for faster and more accurate parallel MRI reconstruction. The new method effectively reduces artifacts and noise using fewer autocalibration signal (ACS) lines.

Keywords:
Co-trainingDisagreement-based semi-supervised learningInfinite impulse response filterParallel MRI reconstruction

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction

Background:

  • Parallel MRI reconstruction relies on autocalibration signal (ACS) for accuracy.
  • Increasing ACS lines improves artifact suppression but prolongs scan time.
  • Single interpolator models in k-space reconstruction can introduce errors.

Purpose of the Study:

  • To develop an efficient parallel MRI reconstruction method.
  • To reduce scan time and improve image quality.
  • To address limitations of single-interpolator models.

Main Methods:

  • Proposed a dual-interpolator strategy based on disagreement-based semi-supervised learning.
  • Utilized two interpolators of different sizes to collaboratively reconstruct missing k-space data.
  • Employed an iterative estimation and re-estimation process leveraging interpolator disagreement.

Main Results:

  • The dual-interpolator method outperformed GRAPPA, SPIRiT, and Nonlinear GRAPPA.
  • Achieved superior performance with a reduced number of ACS lines.
  • Significantly reduced aliasing artifacts and noise in reconstructed MRI images.

Conclusions:

  • The proposed dual-interpolator strategy offers an effective approach for parallel MRI reconstruction.
  • This method enhances image quality while potentially shortening acquisition times.
  • It provides a robust alternative to existing reconstruction techniques, especially with limited ACS data.