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Related Experiment Video

Updated: Oct 1, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

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Learning-based motion artifact removal networks for quantitative mapping.

Xiaojian Xu1, Satya V V N Kothapalli2, Jiaming Liu3

  • 1Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.

Magnetic Resonance in Medicine
|March 8, 2022
PubMed
Summary
This summary is machine-generated.

Two novel deep learning networks, LEARN-IMG and LEARN-BIO, effectively remove motion artifacts from multi-Gradient-Recalled Echo (mGRE) MRI data. These networks enable high-quality quantitative mapping, with LEARN-BIO offering faster computation for clinical use.

Keywords:
MRIconvolutional neural networksdeep learninggradient recalled echomotion correctionself-supervised deep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biophysics

Background:

  • Motion artifacts significantly degrade the quality of quantitative maps derived from multi-Gradient-Recalled Echo (mGRE) MRI.
  • Accurate quantitative mapping is crucial for various clinical applications and research.

Purpose of the Study:

  • To introduce two novel learning-based motion artifact removal networks (LEARN) for correcting motion corrupted mGRE MRI data.
  • To enable the estimation of high-quality quantitative motion- and -inhomogeneity-corrected maps.

Main Methods:

  • Two convolutional neural networks (CNNs), LEARN-IMG and LEARN-BIO, were trained to correct motion artifacts in mGRE sequences.
  • LEARN-IMG corrects motion in complex mGRE images for subsequent map computation.
  • LEARN-BIO directly estimates motion- and -inhomogeneity-corrected maps from magnitude-only mGRE images using a biophysical model.

Main Results:

  • Both CNNs successfully suppressed motion artifacts in synthetic and in vivo mGRE data.
  • Details in the predicted quantitative maps were preserved after artifact removal.
  • Significant reduction in motion artifacts was observed on experimental data.

Conclusions:

  • LEARN-IMG and LEARN-BIO enable high-quality quantitative map estimation, corrected for motion and inhomogeneity.
  • LEARN-IMG processes images for subsequent analysis, while LEARN-BIO directly estimates corrected maps.
  • LEARN-BIO's computational speed suggests potential for broader clinical application.