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An improved method to measure transfer functions using MRI
Michael A Eijbersen1,2, Bart R Steensma1,2, Cornelis A T van den Berg1,2
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Magnetic Resonance in Medicine
|June 11, 2024
Summary
A new MRI method accurately assesses transfer functions without phase assumptions, enabling broader applications for longer leads and higher field strengths. This improved technique enhances MRI safety and diagnostic capabilities.
Area of Science:
- Medical Imaging
- Electromagnetics
- Biophysics
Background:
- Current MRI transfer function assessment relies on the transceive phase assumption (TPA).
- TPA limits applicability to shorter leads and lower field strengths.
- A more generalizable method is needed for diverse MRI applications.
Purpose of the Study:
- To develop a novel MRI-based method for transfer function assessment.
- To overcome limitations of the transceive phase assumption (TPA).
- To enhance applicability for longer leads and higher magnetic field strengths.
Main Methods:
- Determined background electric fields from both B1+ and B1- field distributions.
- Utilized spoiled gradient echo multiflip angle acquisition for B1 field mapping.
- Employed a magnitude squared least squares approach for B1 field modeling.
- Validated the method using EM-simulated data, phantom experiments, and bench measurements.
Main Results:
- Accurate reconstruction of B1 fields, currents, and incident electric fields in simulations.
- Experimental transfer function determination showed high similarity to simulations.
- Experimental results demonstrated accurate transfer function determination despite minor field model deviations.
- The method proved effective for copper wire lengths up to 40 cm.
Conclusions:
- A more generally applicable MRI-based transfer function assessment method is presented.
- The new method avoids phase assumptions, expanding use to longer objects and higher field strengths.
- Improvements in B1 mapping and the solution algorithm enhance the method's robustness.

