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Application of Passive Head Motion to Generate Defined Accelerations at the Heads of Rodents
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Head motion measurement and correction using FID navigators.

Tess E Wallace1, Onur Afacan1, Maryna Waszak2,3,4

  • 1Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.

Magnetic Resonance in Medicine
|July 31, 2018
PubMed
Summary

This study introduces a new method using FID navigators (FIDnavs) for fast, internal head motion measurement in MRI. This approach accurately tracks motion, improving MRI image quality, especially for patients who have difficulty staying still.

Keywords:
FID navigatorsMRI motion measurementcoil sensitivity profilemotion correction

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Physics
  • Biomedical Engineering

Background:

  • Head motion during MRI scans degrades image quality and diagnostic accuracy.
  • Existing motion tracking methods often require external devices or patient-specific calibration.
  • FID navigators (FIDnavs) offer potential for intrinsic motion measurement but require robust extraction techniques.

Purpose of the Study:

  • To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FIDnavs.
  • To enable quantitative estimation of rigid-body motion parameters without external tracking.
  • To facilitate retrospective motion correction in MRI scans.

Main Methods:

  • A forward model of FIDnav signals was calibrated using simulated motion and coil sensitivity models.
  • A FIDnav module was integrated into a 3D FLASH sequence.
  • Rigid-body motion parameters were retrospectively estimated using nonlinear optimization to solve the inverse problem.

Main Results:

  • FIDnav motion estimates showed mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° compared to electromagnetic tracking.
  • Retrospective correction using FIDnav motion estimates significantly improved quantitative image quality metrics.
  • The method was validated in simulated data and in 7 volunteers performing motion paradigms.

Conclusions:

  • Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach.
  • This method provides a practical solution for retrospective motion compensation.
  • It is particularly beneficial for less cooperative patient populations in MRI.