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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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MR sequence design to account for nonideal gradient performance.

Daniel J West1, Felix Glang2, Jonathan Endres3

  • 1Imaging Physics & Engineering Research Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Magnetic Resonance in Medicine
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an optimization method for designing magnetic resonance imaging (MRI) gradient waveforms. The new approach improves image quality by directly correcting k-space samples and respecting hardware limits, especially beneficial for lower-cost MRI systems.

Keywords:
eddy currentsgradient imperfectionsgradient system transfer functionpre‐emphasis

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Physics
  • Biomedical Engineering

Background:

  • Traditional MRI systems assume idealized performance, simplifying pulse sequence design.
  • Eddy currents from gradient fields are typically compensated invisibly via pre-emphasis, down-rating system specifications.
  • This pre-emphasis strategy is suboptimal for resource-limited hardware.

Purpose of the Study:

  • To develop an optimization-based method for designing precompensated gradient waveforms.
  • To explicitly incorporate hardware constraints into waveform design.
  • To improve MRI imaging performance by directly correcting k-space samples.

Main Methods:

  • Numerical optimization of gradient waveforms incorporating a model for system imperfections.
  • Simulation using an exponential eddy current model.
  • Experimental validation on a 7T MRI system using an empirical gradient system transfer function.

Main Results:

  • The proposed method achieved negligible reconstruction errors while respecting gradient system limits.
  • Significant reduction in ghosting artifacts in echo-planar imaging (EPI) was observed.
  • An average reduction of 77% in ghost amplitude was measured in phantom studies.

Conclusions:

  • Numerical optimization of gradient waveforms, considering system imperfections, substantially improves MRI image quality.
  • This method enables more efficient hardware utilization.
  • The approach is particularly valuable for maximizing performance in lower-cost MRI systems.