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Conditional generative adversarial network for 3D rigid-body motion correction in MRI.

Patricia M Johnson1,2, Maria Drangova1,2

  • 1Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.

Magnetic Resonance in Medicine
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Summary
This summary is machine-generated.

Subject motion in MRI causes image artifacts. A deep learning model, a conditional generative adversarial network, was developed to create artifact-free brain images from corrupted data, significantly improving image quality.

Keywords:
MRIconditional generative adversarial networksconvolutional neural networksdeep learningmotion correction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Motion during Magnetic Resonance Imaging (MRI) acquisition is a significant challenge, leading to image blurring and artifacts that degrade diagnostic quality.
  • Existing motion correction techniques often struggle to fully restore image integrity.

Purpose of the Study:

  • To develop and train a conditional generative adversarial network (cGAN) for correcting motion-induced artifacts in MRI brain images.
  • To approach MRI motion correction as an image-to-image translation task, transforming corrupted images into artifact-free representations.

Main Methods:

  • Utilized an open-source MRI dataset (T2*-weighted, FLASH magnitude, and phase images) from 53 patients.
  • Simulated rigid motion (rotations, translations) on image data using random motion profiles.
  • Trained a cGAN, consisting of generator and discriminator networks, with motion-corrupted images and their corresponding ground truth (original) images as training pairs.

Main Results:

  • The cGAN-predicted images showed improved quality compared to motion-corrupted images.
  • Mean absolute error was reduced from 16.4% to 10.8% of the image mean value.
  • Enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were observed in the network's output.

Conclusions:

  • The conditional generative adversarial network effectively corrects motion artifacts in MRI brain images.
  • The developed cGAN provides both quantitative and qualitative improvements in image quality over traditional motion-corrupted data.