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Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE.

Chaoping Zhang1, Alexandra Cristobal-Huerta2, Juan A Hernandez-Tamames2

  • 1Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.

Magnetic Resonance Imaging
|August 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE), a novel method that reduces artifacts and scan time in Gradient Recalled Echo and Spin Echo (GRASE) imaging. APIR4GRASE significantly enhances image quality and reconstruction speed.

Keywords:
GRASEMagnetic resonance imagingParallel imagingReconstructionVirtual coil

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Area of Science:

  • Magnetic Resonance Imaging
  • Image Reconstruction
  • Parallel Imaging

Background:

  • Gradient Recalled Echo and Spin Echo (GRASE) imaging offers fast acquisition but is prone to artifacts and long scan times.
  • Existing reconstruction methods like GRAPPA may not fully address GRASE-specific challenges, particularly with multiple echo types.

Purpose of the Study:

  • To develop and validate a novel method, APIR4GRASE, for reducing artifacts and scan time in GRASE imaging.
  • To optimize sampling patterns and jointly reconstruct gradient echo and spin echo images simultaneously.
  • To improve the overall speed and quality of GRASE imaging.

Main Methods:

  • Introduced the Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method.
  • Treated different echo types as virtual coil channels for joint image reconstruction.
  • Identified and validated optimal acquisition sampling patterns on phantom and in-vivo data.
  • Compared APIR4GRASE against conventional GRASE and GRAPPA reconstruction.

Main Results:

  • APIR4GRASE successfully eliminated modulation artifacts in both phantom and in-vivo experiments.
  • Mean Square Error (MSE) was reduced by 78% (phantom) and 94% (in-vivo) compared to conventional GRASE.
  • Both artifacts and g-factor were reduced compared to GRAPPA reconstruction using a single echo type.

Conclusions:

  • APIR4GRASE significantly improves the speed and quality of GRASE imaging beyond current state-of-the-art methods.
  • The method enables simultaneous reconstruction of both spin echo and gradient echo images.
  • APIR4GRASE offers a robust solution for artifact reduction and accelerated acquisition in GRASE MRI.