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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Related Experiment Video

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Parallel imaging with a combination of sensitivity encoding and generative adversarial networks.

Jun Lv1, Peng Wang1, Xiangrong Tong1

  • 1School of Computer and Control Engineering, Yantai University, Yantai, China.

Quantitative Imaging in Medicine and Surgery
|December 3, 2020
PubMed
Summary

Generative adversarial networks (GAN) effectively reduce g-factor artifacts in Sensitivity encoding (SENSE) MRI reconstructions. This GAN-enhanced SENSE method significantly improves image quality, especially at higher undersampling rates for clinical applications.

Keywords:
Parallel imaginggenerative adversarial networks (GAN)magnetic resonance imaging (MRI) reconstructionsensitivity encoding (SENSE)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) faces speed limitations, addressed by acceleration methods using under-sampled k-space data.
  • Sensitivity Encoding (SENSE) is a common multi-channel imaging technique but suffers from g-factor artifacts at high under-sampling factors.
  • Generative Adversarial Networks (GAN) are explored to mitigate these artifacts in SENSE reconstructions.

Purpose of the Study:

  • To apply GANs for removing g-factor artifacts in SENSE-based MRI.
  • To evaluate the effectiveness of GAN-enhanced SENSE reconstruction compared to existing methods.
  • To assess the impact of GANs on image quality metrics at various under-sampling rates.

Main Methods:

  • A novel method using GANs was developed to process SENSE reconstructions.
  • The approach was validated on a public knee MRI dataset from 20 healthy participants.
  • Image quality was quantitatively assessed using Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), and Normalized Mean Square Error (NMSE), with statistical analysis via paired student's t-test (P<0.01).

Main Results:

  • The proposed SENSE + GAN method significantly outperformed conventional SENSE, Variational Network (VN), and Zero-Filled + GAN (ZF + GAN) across all metrics.
  • SENSE + GAN achieved superior SSIM (0.81±0.06), PSNR (31.90±1.66), and NMSE (0.95±0.34 ×10-7) at up to 6-fold under-sampling.
  • Conventional SENSE showed considerably lower performance (SSIM: 0.40±0.07, PSNR: 22.70±1.99, NMSE: 4.81±1.33 ×10-7).

Conclusions:

  • GANs are feasible for enhancing SENSE MRI reconstruction performance.
  • The proposed method shows significant potential for clinical applications, particularly where high under-sampling rates are necessary.
  • GAN-based artifact reduction offers a promising avenue for faster and higher-quality MRI.