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Deformable cardiac surface tracking by adaptive estimation algorithms.

E Erdem Tuna1, Dominique Franson2, Nicole Seiberlich3

  • 1Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA. eet12@case.edu.

Scientific Reports
|January 25, 2023
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Summary
This summary is machine-generated.

This study introduces a particle filter framework for tracking cardiac surfaces in magnetic resonance imaging (MRI) data. The method enables precise cardiac deformation tracking for interventional cardiovascular procedures.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Biology

Background:

  • Accurate tracking of cardiac surface dynamics is crucial for interventional cardiovascular procedures.
  • Existing methods may lack the precision or real-time capabilities required for MR-guided interventions.
  • Deformable models and advanced filtering techniques offer potential for improved cardiac motion analysis.

Purpose of the Study:

  • To develop and validate a particle filter-based framework for online cardiac surface tracking from magnetic resonance imaging (MRI) slices.
  • To enable precise, real-time monitoring of cardiac deformations for interventional cardiovascular magnetic resonance procedures.
  • To establish a proof-of-concept for using probabilistic models in guiding targeted cardiac interventions.

Main Methods:

  • Utilized a particle filter framework with a low-order parametric deformable model for cardiac surface representation.
  • Employed adaptive filters to model complex cardiac motion within a stochastic dynamic system.
  • Recursively estimated system states over time to track cardiac surface deformations.
  • Validated the method using numerical phantoms and real cardiac MRI datasets under fixed and varying slice planes.

Main Results:

  • Achieved average root-mean-square tracking errors of 2.61 mm (fixed slices) and 3.42 mm (varying slices) on real cardiac MRI data.
  • Demonstrated tracking precision of 3 pixels for points on the cardiac surface.
  • Successfully recovered biventricular deformations, showcasing the framework's capability.
  • The algorithm proved effective in tracking cardiac surface points across different sections.

Conclusions:

  • The developed particle filter framework provides a robust method for tracking cardiac surface deformations from sequential MRI slices.
  • This approach supports the future application in precise, MR-guided interventional cardiovascular procedures, such as intracardiac ablation.
  • The integration of adaptive filters enhances temporal coherence and accuracy in nonrigid cardiac motion tracking.