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[Development of Motion Artifact Generator for Deep Learning in Brain MRI].

Hikari Tsukamoto1, Isao Muro1

  • 1Radiological Technology Department, Clinical Technology Division, Tokai University Hospital.

Nihon Hoshasen Gijutsu Gakkai Zasshi
|May 20, 2021
PubMed
Summary

This study presents a novel simulation method for generating realistic MRI motion artifacts, crucial for training deep learning models. The simulated images demonstrate high similarity to clinical data, enabling efficient artifact reduction development.

Keywords:
braincomputer simulationmagnetic resonance imaging (MRI)motion artifact

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Processing

Background:

  • Motion artifacts significantly degrade Magnetic Resonance Imaging (MRI) quality.
  • Acquiring large datasets of clinical MRI with and without motion artifacts is challenging.
  • Deep learning models require extensive data for effective training in artifact reduction.

Purpose of the Study:

  • To develop a computer simulation method for generating realistic MRI motion artifact images.
  • To create a diverse dataset of simulated artifact images for deep learning model training.
  • To validate the simulation's ability to produce clinically relevant artifact characteristics.

Main Methods:

  • Generated 80 distinct types of motion artifact images via computer simulation, including vertical, horizontal, diagonal, and rotational shifts.
  • Transformed images into k-space data, randomly sampled phase encodings, and performed inverse Fourier transforms to create artifact images.
  • Utilized a U-net deep learning model to verify the reproducibility of simulated artifact images.
  • Quantitatively assessed image quality using Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR).

Main Results:

  • Simulated images achieved an average SSIM of 0.95 and PSNR of 31.5.
  • Clinical artifact images showed comparable results with an average SSIM of 0.96 and PSNR of 31.1.
  • The simulation method demonstrated high fidelity in replicating clinical motion artifact characteristics.

Conclusions:

  • The developed simulation technique efficiently generates a large volume of MRI motion artifact images rapidly.
  • The simulated artifact images are equivalent in quality and characteristics to those found in clinical practice.
  • This method provides a viable solution for augmenting datasets for deep learning-based MRI artifact reduction.