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Receiver phase alignment using fitted SVD derived sensitivities from routine prescans.

Olivia W Stanley1,2, Ravi S Menon1,2, L Martyn Klassen1,2

  • 1Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.

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|August 30, 2021
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Summary
This summary is machine-generated.

A new fitted singular value decomposition (SVD) method improves magnetic resonance imaging (MRI) coil sensitivity estimation for high-field imaging. This technique enables accurate, automatic image combination without manual intervention, crucial for advanced MRI applications.

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Area of Science:

  • Medical Imaging
  • Physics
  • Engineering

Background:

  • Magnetic resonance imaging (MRI) relies on combining signals from multiple radio frequency (RF) coils.
  • Accurate coil sensitivity estimation is crucial for phase alignment and preventing signal interference, especially at higher magnetic fields.
  • Existing methods for coil sensitivity estimation are often impractical for large datasets due to manual intervention or post-acquisition processing.

Purpose of the Study:

  • To develop and validate a novel, automated method for estimating RF coil sensitivities for improved MRI image reconstruction.
  • To address the limitations of current methods in handling large multi-volume datasets and high-field MRI.
  • To create a phase-sensitive combination method suitable for online reconstruction.

Main Methods:

  • A fitted singular value decomposition (SVD) method was proposed, using existing multi-image prescans.
  • Voxel-wise SVD was employed to calculate relative receive sensitivities.
  • Iterative least squares fitting of sensitivities to solid harmonics was used for phase alignment.
  • The method was evaluated on human brain data at 7 Tesla, assessing image singularities and phase signal-to-noise ratio (SNR).

Main Results:

  • The fitted SVD method produced singularity-free images, indicating improved coil combination.
  • It recovered 95-100% of the phase SNR, depending on prescan resolution.
  • The method demonstrated successful application in asymmetrical coils and with subject motion.
  • Online reconstruction was achieved without supervision, enhancing efficiency.

Conclusions:

  • The fitted SVD method provides accurate and robust MRI image combination, particularly for large datasets and high fields.
  • It overcomes limitations of previous methods by automating sensitivity estimation and phase alignment.
  • This technique is suitable for real-time applications and various challenging imaging scenarios, improving MRI data quality and workflow.