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Model-based determination of the synchronization delay between MRI and trajectory data.

Paul Ioan Dubovan1,2, Corey Allan Baron1,2

  • 1Department of Medical Biophysics, Western University, London, Ontario, Canada.

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Accurate magnetic resonance imaging (MRI) requires precise synchronization between MRI data and dynamic magnetic fields. This new algorithm automatically achieves sub-microsecond synchronization, reducing artifacts and improving image reconstruction quality.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Biomedical Engineering
  • Signal Processing

Background:

  • Real-time monitoring of dynamic magnetic fields is now available for measuring MRI k-space trajectories and eddy current-induced fields.
  • Accurate MRI image reconstruction necessitates sub-microsecond synchronization between MRI data and field dynamics, including k-space trajectories and spatially varying fields.
  • Existing methods may struggle to achieve the required precision for synchronization, potentially leading to image artifacts.

Purpose of the Study:

  • To introduce a novel model-based algorithm for automatic synchronization of MRI data and field dynamics.
  • To enable precise sub-microsecond synchronization using only MRI data and measured field dynamics.
  • To improve the accuracy of MRI image reconstructions by mitigating synchronization-related artifacts.

Main Methods:

  • Developed a model-based algorithm that enforces consistency between MRI data, field dynamics, and receiver sensitivity profiles.
  • Employed iterative convex optimization for simultaneously refining the image reconstruction and the synchronization delay.
  • Validated the algorithm using in vivo 7 T MRI scans (spiral and EPI) and numerical simulations with known ground truth delays.

Main Results:

  • The algorithm successfully minimized synchronization delay-related artifacts in both spiral and EPI acquisitions during in vivo scans.
  • Achieved high accuracy in synchronization delay determination, with simulations demonstrating precision within tens of nanoseconds.
  • Demonstrated efficient computation, with in vivo processing times under 30 seconds.

Conclusions:

  • The proposed algorithm effectively automates the determination of synchronization delays between MRI data and measured field dynamics.
  • This automated synchronization is crucial for enhancing the quality and reliability of MRI image reconstructions.
  • The method shows promise for improving real-time MRI applications requiring precise timing.