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Maximising BOLD sensitivity through automated EPI protocol optimisation.

Steffen Volz1, Martina F Callaghan2, Oliver Josephs2

  • 1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.

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This study introduces an automated method using magnetic field simulations to optimize functional magnetic resonance imaging (fMRI) acquisition protocols, significantly reducing susceptibility-induced signal loss for better brain imaging.

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Area of Science:

  • Neuroimaging
  • Magnetic Resonance Imaging (MRI)
  • Functional MRI (fMRI)

Background:

  • Gradient echo echo-planar imaging (GE EPI) is standard for fMRI but suffers from image distortions and Blood-Oxygen-Level-Dependent (BOLD) sensitivity loss due to magnetic field inhomogeneities.
  • Post-processing can correct distortions, but signal dropouts require acquisition-stage solutions.
  • Current optimization methods for reducing susceptibility-related BOLD sensitivity loss are time-consuming and may yield sub-optimal results.

Purpose of the Study:

  • To develop an automated method for optimizing GE EPI protocols to minimize susceptibility-induced BOLD sensitivity loss.
  • To overcome the limitations of current atlas-based and manual optimization techniques.
  • To enable efficient optimization across a wide range of acquisition parameters.

Main Methods:

  • Developed a simulation-based approach using physical models of BOLD sensitivity loss.
  • Utilized a large database of magnetic field (B0) maps from diverse participants to inform simulations.
  • Optimized parameters including resolution, echo times, and slice orientations within a large parameter space.

Main Results:

  • The simulation-based method significantly reduces time and measurement costs compared to traditional approaches.
  • The automated method allows for comprehensive optimization of EPI protocols, considering various factors.
  • Numerical optimization results were validated against prior studies and experimental BOLD sensitivity measurements in healthy volunteers.

Conclusions:

  • The automated, simulation-driven approach provides an efficient and effective method for optimizing fMRI acquisition protocols.
  • This technique addresses susceptibility-induced signal loss, improving BOLD sensitivity and data quality in challenging brain regions.
  • The developed method has the potential to enhance the reliability and accuracy of fMRI studies across various research applications.