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TU‐C‐17A‐12: Towards a Passively Optimized Phase‐Space Monte Carlo (POPMC) Treatment Planning Method: A Proof of Principle
Author(s) -
Yang Y M,
Zankowski C,
Svatos M,
Bednarz B
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889287
Subject(s) - monte carlo method , voxel , fluence , dosimetry , computer science , mathematical optimization , algorithm , physics , nuclear medicine , mathematics , optics , artificial intelligence , statistics , medicine , laser
Purpose: The advent of on‐line/off‐line adaptive, and biologically‐conformal radiation therapy has led to a need for treatment planning solutions that utilize voxel‐specific penalties, requiring optimization over a large solution space that is performed quickly, and the dose in each voxel calculated accurately. This work proposes a “passive” optimization framework, which is executed concurrently during Monte Carlo dose calculation, evaluating the cost/benefit of each history during transport, and creates a passively optimized fluence map. Methods: The Monte Carlo code Geant4 v9.6 was used for this study. The standard voxel geometry implementation was modified to support the passive optimization framework, with voxel‐specific optimization parameters. Dose‐benefit functions, which will increase a particle history’s weight upon dose deposition, were defined in a central collection of voxels to effectively create target structures. Histories that deposit energy to voxels are reweighted based on a voxel’s dose multiplied by its cost/benefit value. Upon full termination of each history, the dose contributions of that history are reweighted to reflect a contribution proportional to the history’s final weight. A parallel‐planar 1.25 MeV photon fluence is transported through the geometry, and re‐weighted at each dose deposition step. The resulting weight is tallied with the incident spatial and directional coordinates in a phase‐space distribution. Results: A uniform incident fluence was reweighted during MC dose calculations to create an optimized fluence map which would generate dose profiles in target volumes that exhibit the same dose characteristics as the prescribed optimization parameters. An optimized dose profile, calculated concurrently with the phase‐space, reflects the resulting dose distribution. Conclusion: This study demonstrated the feasibility of passively optimizing an incident fluence map during Monte Carlo dose calculations. The flexibility of the voxel‐specific optimization framework allows a variety and combination of optimization parameters to be calculated for each voxel at every transportation step. This work is partially supported by Varian. This work is partially supported by the NIH.