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Removing unwanted background phase with a reference phantom for applications in susceptibility quantification.

He Xie1, Yu-Chung Norman Cheng2, Saifeng Liu3

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This study introduces a novel method to accurately remove background phase in imaging using a reference phantom. The technique effectively quantifies magnetic susceptibility without overcorrection, proving feasible for in vivo applications.

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Image Post-processing

Background:

  • Accurate quantification of magnetic susceptibility is crucial in MRI.
  • Background phase in MRI data can obscure important signals and lead to inaccuracies.
  • Existing methods may overcorrect for phase shifts, affecting object of interest signals.

Purpose of the Study:

  • To propose and validate a method for removing background phase in MRI.
  • To accurately quantify magnetic susceptibility without overcorrecting signals from objects of interest.
  • To investigate factors influencing phase accuracy during imaging and post-processing.

Main Methods:

  • A reference phantom was used to remove eddy currents and background phase.
  • Least squares fitting was employed to quantify magnetic susceptibility.
  • Simulated spheroids, phantoms (cylindrical, Gd-DTPA filled straws), and a head model were used for validation.
  • In vivo human head data was acquired to demonstrate feasibility.

Main Results:

  • Simulations accurately quantified magnetic moments of spheroids and phantoms, unaffected by partial volume effects.
  • Measured susceptibility values for agarose and Gd-DTPA were highly accurate.
  • Residual phase was minimal, well within acceptable limits.
  • Accurate susceptibility values were obtained for simulated and actual human heads.

Conclusions:

  • The proposed method reliably removes background phase without overcorrection, as demonstrated in phantom studies.
  • The technique is feasible for in vivo applications, as shown by the human head example.
  • Accurate magnetic susceptibility quantification is achievable with this novel approach.