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This study introduces a specialized magnetic resonance imaging (MRI) gradient coil for the magnetic resonance hydrophone (MRH) technique. This coil enables precise transcranial focused ultrasound neuromodulation by imaging ultrasonic vibrations within the human cortex.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Acoustics

Background:

  • Transcranial focused ultrasound (tFUS) neuromodulation requires precise targeting based on individual skull anatomy.
  • Current methods lack real-time MRI-based feedback to guide tFUS delivery.
  • Acoustic monitoring techniques are needed to ensure safety and efficacy of tFUS.

Purpose of the Study:

  • To develop and characterize an MRI-based system for monitoring ultrasonic vibrations during tFUS.
  • To create a specialized gradient coil for the magnetic resonance hydrophone (MRH) technique.
  • To assess the feasibility of tailoring tFUS neuromodulation using patient-specific skull morphology.

Main Methods:

  • A 60 mm diameter, pancake-style gradient coil was designed to sensitize MRI images to ultrasonic vibrations.
  • The coil was integrated with a 500 kHz custom ultrasonic transducer.
  • Benchtop experiments characterized the magnetic field gradient and acoustic field; in silico methods estimated acoustic standing waves.
  • Resulting MR images were analyzed to estimate acoustic pressures and compared to conventional hydrophone measurements.

Main Results:

  • The MRH system successfully visualized acoustic displacements as sinusoidal phase patterns in MR images.
  • Acoustic pressure estimations from MR images showed modulation by both acoustic and magnetic fields.
  • Pressure measurement uncertainty due to electronic noise increased exponentially with depth, ranging from 20-100 kPa between 0-30 mm depth.
  • The MRH system underestimated hydrophone measurements by an average of 12 kPa (SD 21 kPa).

Conclusions:

  • The developed gradient coil enables MRI-based monitoring of ultrasonic vibrations for tFUS applications.
  • The MRH technique shows potential for real-time feedback in tFUS neuromodulation.
  • Further optimization is needed to reduce depth-dependent uncertainty and improve pressure estimation accuracy.