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[Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data].

Isao Muro1, Syuntaro Shimizu1, Hikari Tsukamoto1

  • 1Division of Radiology, Department of Clinical Technology, Tokai University Hospital.

Nihon Hoshasen Gijutsu Gakkai Zasshi
|January 20, 2022
PubMed
Summary

Deep learning effectively reduces motion artifacts in brain MRI scans. The U-Net model achieved high image quality, preserving structural integrity and signal-to-noise ratio.

Keywords:
brain magnetic resonance imagingcomputer simulationdeep learning convolutional neural networkmotion artifact

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Motion artifacts are a common problem in brain MRI, degrading image quality.
  • Acquiring large clinical datasets for training artifact reduction models is challenging and time-consuming.

Purpose of the Study:

  • To evaluate the efficacy of deep learning (DL) techniques for reducing motion artifacts in brain MRI.
  • To compare the performance of different DL models in artifact removal.

Main Methods:

  • Generated simulated brain MRI images with various motion artifacts for DL model training.
  • Utilized 20 volunteers' original brain MRI images to create diverse artifact datasets.
  • Compared three DL models: U-Net, DnCNN, and Win5RB, assessing Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).

Main Results:

  • The U-Net model demonstrated superior performance in reducing motion artifacts.
  • Achieved high image quality metrics with U-Net: SSIM of 0.978 and PSNR of 32.5 dB.

Conclusions:

  • Deep learning, particularly the U-Net architecture, provides an effective method for motion artifact reduction in brain MRI.
  • This approach successfully reduces artifacts without compromising overall image quality.