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Automatic motion estimation using flow parameters for dynamic contrast-enhanced ultrasound.

Guillaume Barrois1, Alain Coron, Olivier Lucidarme

  • 1Laboratoire d'Imagerie Biomédical Sorbonne Universités, UPMC Univ Paris 6, UMR, U1146 INSERM, and UMR7371 CNRS, F-75005, Paris, France.

Physics in Medicine and Biology
|February 17, 2015
PubMed
Summary
This summary is machine-generated.

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A new operator-free method for dynamic contrast-enhanced ultrasound (DCE-US) accurately corrects motion. This motion/quantification (M/Q) method improves functional flow parameter reliability in DCE-US imaging.

Area of Science:

  • Medical Imaging
  • Ultrasound Technology
  • Biomedical Engineering

Background:

  • Motion artifacts in dynamic contrast-enhanced ultrasound (DCE-US) compromise functional flow quantification accuracy.
  • Existing motion correction methods rely on user-selected reference images, which may not represent the entire sequence.
  • Image-based registration neglects crucial functional flow information inherent in DCE-US sequences.

Purpose of the Study:

  • To introduce an operator-free method integrating motion estimation and flow-parameter quantification (M/Q method) for DCE-US.
  • To address limitations of conventional reference-image-based registration in DCE-US motion correction.
  • To evaluate the performance of the M/Q method against conventional techniques using simulated and in vivo data.

Main Methods:

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  • Developed an operator-free M/Q method within a single mathematical framework using a multiplicative noise model for DCE-US.
  • Iteratively estimated motion and flow parameters by computing likelihood within the noise model.
  • Employed non-parametric temporal smoothing for final registration, contrasting with conventional mutual information-based registration.

Main Results:

  • The M/Q method demonstrated significantly lower Dice coefficients and Hausdorff distances on simulated DCE-US data (p < 0.05).
  • In vivo analysis showed the M/Q method outperformed conventional methods in mean Dice and Hausdorff distance for 80% of sequences.
  • The M/Q method exhibited superior performance in standard deviation of Dice and Hausdorff distance for 87% of in vivo sequences.

Conclusions:

  • The proposed operator-free M/Q method offers a robust and reliable approach for motion correction in DCE-US.
  • This integrated framework enhances the accuracy and consistency of functional flow parameter quantification.
  • The M/Q method presents a significant advancement over conventional registration techniques for DCE-US imaging.