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SU-E-T-618: Error Compensated Sparse Optimization for Fast Radiosurgery Treatment Planning.

T Viulet1,2, A Schlaefer1,2

  • 1Graduate School for Computing in Medicine and Life Sciences, University of Lübeck.

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|May 19, 2017
PubMed
Summary
This summary is machine-generated.

Sparse grid radiosurgery planning significantly reduces optimization time while maintaining dose accuracy. By carefully adjusting bounds, this method enables faster, interactive treatment planning without compromising results.

Keywords:
GraduatesMedical treatment planningRadiosurgery

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Computational Biology

Background:

  • Radiosurgical treatment planning relies on accurate dose distribution computation.
  • High-resolution grids used for computation lead to large optimization problems and long runtimes.
  • Sparse grid approaches offer a potential solution to reduce computational burden.

Purpose of the Study:

  • To investigate the efficacy of a sparse grid approach for radiosurgical treatment planning.
  • To estimate and compensate for expected deviations from dose bounds in sparse grid planning.
  • To reduce optimization times for radiosurgery treatment planning.

Main Methods:

  • Developed a method to estimate hotspot error distribution by analyzing dose deviations for random beam configurations.
  • Adjusted bounds for voxels within the planning target volume (PTV) based on estimated deviations.
  • Applied constrained optimization to optimize PTV bounds on sparse grids (2, 4, 8mm) for a prostate case, with results computed on a 1mm grid.

Main Results:

  • Sparse grid planning achieved comparable results to high-resolution grids with carefully balanced bounds.
  • Optimization times were substantially reduced: 141.1 min (2mm), 22.6 min (4mm), and 3.4 min (8mm) compared to high-resolution grids.
  • Adjusting upper bounds on 4mm and 8mm grids resulted in minimal volume exceeding the bound (0.75% and 2.2%) with no change in coverage.

Conclusions:

  • Sparse grid planning is a viable alternative to high-resolution grids for radiosurgery.
  • Careful balancing of bounds is crucial for maintaining dose accuracy with sparse grids.
  • This approach significantly decreases optimization times, facilitating interactive treatment planning.