Commissioning of Aktina SRS cones and dosimetric validation of the RayStation photon Monte Carlo dose calculation algorithm.
Andy SchofieldMatthew NewallDean InwoodSimon DownesStéphanie CordePublished in: Physical and engineering sciences in medicine (2023)
Clinical implementation of SRS cones demands particular experimental care and dosimetric considerations in order to deliver precise and safe radiotherapy to patients. The purpose of this work was to present the commissioning data of recent Aktina cones combined with a 6MV flattened beam produced by an Elekta VersaHD linear accelerator. Additionally, the modelling process, and an assessment of dosimetric accuracy of the RayStation Monte Carlo dose calculation algorithm for cone based SRS was performed. There are currently no studies presenting beam data for this equipment and none that outlines the modelling parameters and validation of dose calculation using RayStation's photon Monte Carlo dose engine with cones. Beam data was measured using an SFD and a microDiamond and benchmarked against EBT3 film for cones of diameter 5-39 mm. Modelling was completed and validated within homogeneous and heterogeneous phantoms. End-to-end image-guided validation was performed using a StereoPHAN™ housing, an SRS MapCHECK and EBT3 film, and calculation time was investigated as a function of statistical uncertainty and field diameter. The TPS calculations agreed with measured data within their estimated uncertainties and clinical treatment plans could be calculated in under a minute. The data presented serves as a reference for others commissioning Aktina stereotactic cones and the modelling parameters serve similarly, while providing a starting point for those commissioning the same TPS algorithm for use with cones. It has been shown in this work that RayStation's Monte Carlo photon dose algorithm performs satisfactorily in the presence of SRS cones.
Keyphrases
- monte carlo
- electronic health record
- machine learning
- big data
- deep learning
- end stage renal disease
- primary care
- early stage
- chronic kidney disease
- neural network
- mental health
- data analysis
- quality improvement
- small cell lung cancer
- artificial intelligence
- room temperature
- high resolution
- health insurance
- prognostic factors
- optic nerve
- pain management
- replacement therapy
- optical coherence tomography
- locally advanced
- case report
- combination therapy