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Program Information

Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

D Hall

D Hall1*, J Perl2 , J Schuemann1 , B Faddegon3 , H Paganetti1 , (1) Massachusetts General Hospital, Boston, MA, (2) Stanford Linear Accelerator Center, Menlo Park, CA, (3) UC San Francisco, San Francisco, CA


SU-F-T-139 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4).

Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations.

Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances.

The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user.

Funding Support, Disclosures, and Conflict of Interest: This work was funded by National Cancer Institute grant R01 CA 140735.

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