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Steps in the Modeling Process01:14

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Mechanism of Breathing III: The Accessory Muscles01:21

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Related Experiment Video

Updated: Oct 13, 2025

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns
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How should we model and evaluate breathing interplay effects in IMPT?

Oscar Pastor-Serrano1, Steven Habraken2,3, Danny Lathouwers1

  • 1Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands.

Physics in Medicine and Biology
|November 10, 2021
PubMed
Summary

Statistical methods are crucial for evaluating breathing interplay effects in Intensity Modulated Proton Therapy (IMPT). This study presents a robust method to model respiratory motion and assess treatment plan robustness, highlighting the need for accurate breathing variability analysis.

Keywords:
4DCT robust optimizationITV robust optimizationIntensity Modulated Proton Therapy (IMPT)breathing interplay effectsbreathing motionstatistical evaluation

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Breathing motion during Intensity Modulated Proton Therapy (IMPT) introduces interplay effects, complicating accurate dose delivery.
  • Assessing these effects requires statistical methods that capture breathing variability for robust clinical evaluation.

Purpose of the Study:

  • To develop and present a statistical method for modeling intra-fraction respiratory motion in IMPT.
  • To assess clinical aspects of interplay evaluation, including irregular breathing, sensitivity to breathing changes, and required statistical power.

Main Methods:

  • Comparison of two data-driven methods for generating artificial patient-specific breathing signals: sinusoidal and deep learning models.
  • Investigation of the relationship between interplay doses and breathing parameters, analyzing sensitivity to small breathing variations.
  • Application of the statistical method to analyze the interplay robustness of 4DCT and Internal Target Volume (ITV) plans in lung cancer patients.

Main Results:

  • Deep learning models generate more realistic breathing signals than sinusoidal models.
  • Small changes in breathing period significantly impact dose distribution, revealing a highly fluctuating interplay dose-parameter relationship.
  • Limited sampling for interplay statistics introduces greater error than using sinusoidal models or ignoring breathing hysteresis.

Conclusions:

  • The developed statistical method provides a trustworthy quantification of interplay effects in IMPT.
  • 4DCT plans demonstrate better interplay robustness compared to ITV plans, which systematically fail robustness requirements even with 33 fractions.