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GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions.

Robert Xu1, Graham A Wright1

  • 1Physical Sciences Platform and Schulich Research Centre, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.

Computer Methods and Programs in Biomedicine
|October 1, 2016
PubMed
Summary

A new dynamic motion model corrects respiratory motion during MRI-guided cardiac interventions. This improves anatomical accuracy for more precise procedures.

Keywords:
Cardiac interventionsElectrophysiologyGPU accelerationMagnetic resonance imagingRespiratory motion modeling

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Area of Science:

  • Medical Imaging
  • Cardiovascular Interventions
  • Computational Anatomy

Background:

  • Intra-procedural respiratory motion limits anatomical accuracy in MRI-guided cardiac interventions.
  • Pre-procedural roadmap images lack real-time motion correction.
  • Accurate cardiac interventions require precise anatomical visualization.

Purpose of the Study:

  • To develop and evaluate a dynamic motion model for correcting respiratory motion during MRI-guided cardiac interventions.
  • To improve the accuracy of MRI guidance by integrating prior anatomical data with real-time motion information.
  • To enhance the precision of cardiac interventions through advanced motion compensation techniques.

Main Methods:

  • Implemented a GPU-accelerated image registration algorithm for rapid motion estimation.
  • Developed an adaptive dynamic motion model updated each heart cycle.
  • Utilized the model to predict and correct respiratory motion for realigning imaging data.

Main Results:

  • GPU algorithm achieved completion in 176.9 ± 14.0 ms, 139x faster than CPU.
  • Feasible to update the dynamic motion model every heart cycle.
  • Improved registration accuracy from 86.0 ± 7.5% to 93.0 ± 3.3% for variable breathing patterns.

Conclusions:

  • Demonstrated the feasibility of a dynamic motion correction framework for MRI-guided cardiac interventions.
  • The proposed method significantly improves registration accuracy.
  • Potential for more accurate and effective MRI-guided cardiac interventions in the future.