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Related Experiment Video

Updated: May 20, 2026

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats
08:22

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats

Published on: September 19, 2025

Patient specific respiratory motion modeling using a 3D patient's external surface.

Hadi Fayad1, Tinsu Pan, Olivier Pradier

  • 1INSERM UMR1101, LaTIM, CHU Morvan, Brest F-29200, France. Hadi.Fayad@univ-brest.fr

Medical Physics
|July 5, 2012
PubMed
Summary

This study shows that using external patient surface data improves respiratory motion modeling for radiation therapy. This patient-specific approach is more accurate than traditional methods relying on 1D respiratory signals.

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Image-guided therapy

Background:

  • Accurate respiratory motion modeling is crucial for reducing errors in radiation therapy.
  • Current models often rely on 1D external signals, which may have limited correlation with internal organ motion.
  • Patient-specific models are needed to account for individual respiratory variations.

Purpose of the Study:

  • To develop and evaluate a continuous, patient-specific respiratory motion model using external surface information.
  • To compare the performance of surface-based models against traditional models using 1D respiratory signals (amplitude and phase).
  • To assess the model's ability to handle irregular breathing patterns.

Main Methods:

  • Utilized 4D CT data from ten patients, extracting surface information via segmentation.

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Last Updated: May 20, 2026

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats
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Published on: September 19, 2025

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Published on: November 11, 2020

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  • Constructed patient-specific models using principal component analysis.
  • Related internal motion (deformation matrices) to external motion surrogates (surface data vs. 1D RPM signals).
  • Assessed model performance using repeated 4D CT acquisitions and static CTs at end-inspiration/expiration.

    • Main Results:

    • Surface-based models demonstrated superior performance (CC: 0.998 ± 0.0006, ME: 1.35 ± 0.21 mm) compared to 1D signal-based models (CC: 0.971 ± 0.02, ME: 1.64 ± 0.28 mm).
    • The surface model accurately estimated motion in repeated scans and extreme respiratory phases (full inspiration/expiration).
    • Models using both amplitude and phase from 1D signals outperformed models using only one parameter.

    Conclusions:

    • External patient surface data offers a promising approach for constructing accurate, patient-specific respiratory motion models.
    • Surface-based modeling significantly enhances the estimation of internal structure motion during respiration.
    • This method can be implemented with currently available devices for improved radiotherapy accuracy.