Representing Range Compensators with Computational Geometry in TOPAS

Forrest Iandola1,2 and Joseph Perl1

1 SLAC National Accelerator Laboratory2 University of Illinois at Urbana-Champaign

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Overview

• Medical Physics in 30 Seconds• Introduction to TOPAS• What is a range compensator?• Subtraction Solid geometry• Modeling compensators with Subtraction Solids• Approximation with polyhedrons for (potentially)

faster performance

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Medical Physics in 30 Seconds

• Goal: kill cancer with radiation• Deliver radiation with protons, photons,

other particles, or ions• Monte Carlo simulation of proton therapy

beams is an up-and-coming field

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Introduction to TOPAS

TOPAS (Tool for Particle Simulation)

• TOPAS = Monte Carlo simulation of radiation therapy beamlines

• User can easily customize beamline for specific treatment facilities

• Uses Geant4 for the “real” physics

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What is a Range Compensator?

• A radiation therapy beamline collimates the beam and produces a specific energy spread

• Range compensator produces an energy spread• Construction: drill a number of holes out of a

cylinder of lucite• Each drill hole may have a unique depth• “The thickness of the Lucite [plastic] will

proportionally reduce the depth [energy] of the protons”1

1 http://neurosurgery.mgh.harvard.edu/protonbeam/

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What is a Range Compensator?

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Subtraction Solids

• Geant4 supports boolean solid combinatorial geometry– Subtraction solids– Union solids

• It’s as simple as newSolid = Solid1 - Solid2• Overlap among subtracted solids is

acceptable• Solids can be recursively subtracted

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Compensator with Subtraction Solids

Compensator comprised of a bigCylinder with n holes subtracted:

newSolid1 = bigCylinder - smallCylinder1newSolid2 = newSolid1 - smallCylinder2…Compensator = newSolid(n-1) - smallCylinder(n)

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Compensator with Subtraction Solids

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Approximation for Performance Gains

• Approximate the drill holes with a collection of hexagons

• Lack of overlap among hexagons allows us to model all hexagons as a single polyhedron

• Future work: evaluate performance benefits (and accuracy reduction) with polyhedron method

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Approximation for Performance Gains

Subtraction Solid Polyhedron

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Approximation for Performance Gains

• Future work: evaluate performance benefits (and accuracy reduction) with polyhedron method

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Acknowledgements

• Harald Paganetti (Massachusetts General Hospital and Harvard University)

• Jan Schuemann (Massachusetts General Hospital and Harvard University)

• Jungwook Shin (UC San Francisco)• Bruce Faddegon (UC San Francisco)• DOE and NIH for generous support

Contact: forrest@slac.stanford.edu