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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Computational Biology

Background:

  • Internal organ motion during radiation delivery can compromise treatment efficacy and patient safety.
  • Respiration-induced motion is a significant challenge in treating lung and abdominal cancers, causing tumor displacement and organ deformation.
  • Real-time on-board MRI scanners in modern radiotherapy devices offer opportunities for motion management.

Purpose of the Study:

  • To develop, calibrate, and test predictive models for respiration-induced anatomical motion.
  • To utilize real-time MRI data for short-term motion trajectory forecasting during radiation delivery.
  • To enable real-time tracking and adaptive re-optimization of intensity-modulated radiation therapy.

Main Methods:

  • Development of a semi-Markov model to predict respiratory cycle phase transitions.
  • Implementation of a Markov model for forecasting transitions between respiratory cycles.
  • Integration of real-time MRI imaging for motion data acquisition and model input.

Main Results:

  • Accurate short-term forecasting of respiration-induced anatomical motion.
  • Demonstrated potential for longer-term motion prediction using sequential Markov models.
  • Validation of predictive models using real-time MRI data.

Conclusions:

  • Predictive motion models utilizing real-time MRI can enhance radiotherapy precision.
  • Accurate motion forecasting supports adaptive radiotherapy strategies for improved tumor targeting.
  • This approach holds promise for reducing underdosing of cancer cells and overdosing of normal tissues.