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WE-G-BRCD-07: IMRT Re-Planning by Adjusting Voxel-Based Weighting Factors for Adaptive Radiotherapy.

N Li1,2, M Zarepisheh1,2, Z Tian1,2

  • 1University of California, San Diego, La Jolla, CA.

Medical Physics
|May 19, 2017
PubMed
Summary

This study presents an automated algorithm for adaptive radiation therapy (ART) re-planning. The method efficiently adjusts voxel weighting factors to improve dose distribution, reducing manual effort and treatment time.

Keywords:
Adaptive radiation therapyAnatomyCancerDosimetryIntensity modulated radiation therapyMedical radiation safetyRadiation monitoringRadiation therapyRadiation treatmentTissues

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Adaptive radiation therapy (ART) aims to minimize normal tissue toxicity and enhance tumor control by adapting treatments to patient anatomy.
  • Efficient re-planning is crucial for ART, but manual parameter tuning is time-consuming.
  • Utilizing existing plan data like dose distribution and DVH curves can streamline re-planning.

Purpose of the Study:

  • To develop an automated re-planning algorithm for ART.
  • To automate voxel weighting factor adjustments for DVH curve optimization.
  • To generate re-plans comparable or superior to original plans.

Main Methods:

  • An iterative algorithm combining fluence map optimization and heuristic weighting factor adjustment.
  • Outer loop adjusts voxel weights based on DVH deviations from the original plan.
  • Inner loop optimizes dose using fixed weights; process repeats until convergence or max iterations.
  • Algorithm implemented on GPU for high efficiency.

Main Results:

  • Tested on three head-and-neck cancer cases.
  • Resulting DVH curves were superior to original plans after 30 iterations.
  • Re-optimization process completed in approximately 30 seconds.

Conclusions:

  • Automated adjustment of voxel weighting factors using DVH comparison is a promising ART re-planning strategy.
  • This approach effectively bypasses the need for manual, trial-and-error planning.
  • The developed algorithm offers significant time savings for ART re-planning.