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Technical Note: A novel leaf sequencing optimization algorithm which considers previous underdose and overdose events

Eric Wisotzky1, Ricky O'Brien1, Paul J Keall1

  • 1Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.

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|January 10, 2016
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Summary
This summary is machine-generated.

This study introduces an improved algorithm for multileaf collimator (MLC) tracking radiotherapy. The new method minimizes radiation dose errors by considering past underdose and overdose events, enhancing treatment accuracy.

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Image-Guided Therapy

Background:

  • Multileaf collimator (MLC) tracking in radiotherapy is challenged by real-time target motion, necessitating online recomputation of beam patterns.
  • Existing MLC tracking algorithms often overlook past errors, potentially leading to cumulative dose inaccuracies.

Purpose of the Study:

  • To develop and enhance an algorithm for MLC tracking radiotherapy that minimizes underdose and overdose regions.
  • To incorporate past dose events into the optimization process to avoid repeated errors.

Main Methods:

  • Introduced a cumulative underdose/overdose map to log dose events in specific regions.
  • Evaluated the algorithm's performance by simulating conformal and intensity-modulated radiotherapy (IMRT) plans with patient-measured tumor motion traces.
  • Quantified improvements using L1-norm minimization of overdose and underdose.

Main Results:

  • Simulations demonstrated up to an 8.5% improvement in L1-norm after 100 MLC modification steps for conformal shapes.
  • Experimental results showed comparable improvements for conformal and IMRT treatment plans.

Conclusions:

  • A novel leaf sequencing optimization algorithm for MLC tracking radiotherapy, considering previous dose events, has been developed.
  • The algorithm effectively reduces underdose and overdose occurrences in both conformal and IMRT delivery.