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Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging.

Ki Hwan Kim1,2, Sunghun Seo1, Won-Joon Do1

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.

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Summary

This study introduces a novel MRI sampling strategy, acquiring data across different phase-encoding directions to accelerate imaging. This method enhances multicontrast and bSSFP MRI, improving efficiency and performance in real-world applications.

Keywords:
balanced steady-state free precessioncompressed sensing, deep learningmulticontrast MRIphase-encoding dimensionsampling pattern

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Imaging Technology
  • Signal Processing

Background:

  • Accelerating Magnetic Resonance Imaging (MRI) acquisitions is crucial for improving patient comfort and throughput.
  • Current acceleration techniques often face limitations in image quality or applicability across diverse imaging contrasts.
  • Efficient sampling strategies are needed to maximize data acquisition within reduced scan times.

Purpose of the Study:

  • To propose and evaluate a novel MRI sampling strategy for accelerating multi-acquisition MRI.
  • To assess the performance of this strategy across various MRI contrasts and imaging techniques.
  • To demonstrate the practical utility of the proposed method in real-time and retrospective clinical scenarios.

Main Methods:

  • A new sampling strategy was developed, acquiring data along different phase-encoding directions across multiple MRI acquisitions.
  • The strategy was tested using multicontrast (T1, T2, proton density) and multiple phase-cycled balanced steady-state free precession (bSSFP) imaging.
  • Convolutional neural networks were employed with central and random sampling patterns, validated on in vivo and public MRI datasets.

Main Results:

  • The proposed sampling strategy demonstrated superior performance compared to conventional methods sampling along the same phase-encoding direction.
  • Improvements were observed across multicontrast and multiple PC-bSSFP imaging, irrespective of sampling pattern or dataset.
  • Prospective in vivo applications confirmed the strategy's advantage in real-world MRI acquisitions, with preliminary compressed sensing trials also showing enhancement.

Conclusions:

  • Acquiring MRI data along different phase-encoding directions across multiple acquisitions is an effective strategy for accelerating multi-acquisition MRI.
  • This approach offers significant advantages regardless of sampling pattern or dataset, with potential for further enhancement via transfer learning.
  • The proposed method provides a robust solution for faster MRI acquisition in clinical practice.