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Divergence and Curl of Magnetic Field01:26

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Virtual coil augmentation for MR coil extrapoltion via deep learning.

Cailian Yang1, Xianghao Liao1, Liu Zhang1

  • 1Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.

Magnetic Resonance Imaging
|October 14, 2022
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Summary

This study introduces an AI method using dummy variable technology to virtually expand magnetic resonance imaging (MRI) coils. This technique enhances parallel imaging reconstruction, accelerating scan times and improving image quality.

Keywords:
Parallel imagingReversible networkVariable augmentationVirtual coil

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) faces challenges in achieving high-quality images due to hardware, scan time, and throughput limitations.
  • Clinical applications often require faster scanning without compromising image fidelity.

Purpose of the Study:

  • To propose an artificial intelligence (AI) based method for virtual coil expansion in MRI.
  • To enhance parallel imaging reconstruction performance and accelerate MRI scans.

Main Methods:

  • Utilizing dummy variable technology to expand receive coils in both image and k-space domains.
  • Incorporating variable augmentation technology for high-dimensional prior information and deep feature extraction.
  • Employing a sum of squares (SOS) objective function to address k-space data deficiency and speed up convergence.

Main Results:

  • Demonstrated significant potential in accelerating parallel imaging reconstruction.
  • Showcased the effectiveness of AI-driven coil expansion for improving MRI data.
  • Validated the benefits of variable augmentation and SOS objective function in network design.

Conclusions:

  • The proposed AI method effectively expands MRI coils virtually, enhancing parallel imaging.
  • This approach offers a promising solution for faster MRI acquisition with improved image quality.
  • The technique holds potential for broader clinical adoption in accelerating MRI workflows.