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Data truncation artifact reduction in MR imaging using a multilayer neural network.

H Yan1, J Mao

  • 1Dept. of Electr. Eng., Sydney Univ., NSW.

IEEE Transactions on Medical Imaging
|January 1, 1993
PubMed
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Truncation artifacts in magnetic resonance images (MRI) can be reduced using a novel multilayer neural network. This method predicts missing high-frequency data from low-frequency components to improve image quality.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Signal Processing

Background:

  • Conventional Fourier transform methods for magnetic resonance image (MRI) reconstruction can lead to truncation artifacts.
  • These artifacts arise from insufficient high-frequency data, degrading image quality.

Purpose of the Study:

  • To present a novel method for reducing truncation artifacts in MRI.
  • To leverage multilayer neural networks for artifact reduction.

Main Methods:

  • A multilayer neural network with at least one nonlinear hidden layer and one linear output layer was designed.
  • The network predicts missing high-frequency image components using available low-frequency data.

Main Results:

  • The proposed neural network method effectively reduces truncation artifacts in MRI.

Related Experiment Videos

  • Simulation experiments demonstrated the efficacy of the artifact reduction technique.

    • Conclusions:

    • Multilayer neural networks offer a promising approach for mitigating truncation artifacts in MRI reconstruction.
    • This method enhances MRI image quality by reconstructing missing high-frequency information.