Model for the correction of motion-induced phase errors in multishot diffusion-weighted-MRI of the head: are

R L O'Halloran1, S Holdsworth, M Aksoy

  • 1Department of Radiology, Stanford University, Stanford, California, USA. rafaelo@stanford.edu

Insights

Motion during diffusion-weighted imaging causes phase errors. This study models these errors, separating rigid and non-rigid motion, and finds non-rigid motion correlates with pulse waveforms, suggesting repeatable patterns for improved MRI.

Area of Science:

  • Medical Imaging
  • Biophysics
  • Neuroimaging

Background:

  • Multishot diffusion-weighted imaging (DWI) is susceptible to intershot phase inconsistencies.
  • These inconsistencies arise from motion during diffusion-encoding gradients, complicating image analysis.
  • Accurate modeling of motion-induced phase errors is crucial for robust DWI.

Purpose of the Study:

  • To present a model separating rigid-body and nonrigid-body motion-induced phase errors in brain DWI.
  • To investigate the repeatability of nonrigid-body motion-induced phase errors, assuming cardiac pulsation as the source.
  • To validate the assumption of repeatable nonrigid-body motion patterns in healthy volunteers.

Main Methods:

  • Developed a model to differentiate rigid and nonrigid motion effects on phase errors in DWI.
  • Assessed the repeatability of nonrigid motion-induced phase errors by comparing beat-to-beat variations.
  • Quantified phase error repeatability in three healthy volunteers using pulse-oximeter waveforms.

Main Results:

  • The developed model successfully separated rigid and nonrigid motion components.
  • Nonrigid-body motion-induced phase errors demonstrated significant repeatability across subjects and slices.
  • An ~83% correlation was found between nonrigid-body motion-induced phase and pulse-oximeter waveforms (P < 0.05).

Conclusions:

  • Nonrigid-body motion in DWI is largely attributable to cardiac pulsation and exhibits repeatable patterns.
  • The findings support the assumption of repeatable beat-to-beat motion for modeling phase errors.
  • This research contributes to improving the accuracy and reliability of diffusion-weighted MRI.