Open Collections

Incorporating microdosimetry into radiation therapy treatment planning with multi-scale Monte Carlo simulations

University of British Columbia

In order to choose and design optimal treatment plans for radiation therapy, it is necessary to employ models that can predict the tissue response to the ionizing radiation. The conventional models are based on the radiological absorbed dose, which does not account for the effect of radiation damage clustering on cellular response – a more mechanistic model can lead to better metrics for treatment planning. This dissertation presents a novel method for performing multi-scale Monte Carlo simulations to obtain microdosimetric information for patient specific treatments. This is done by using a track structure Monte Carlo simulation in the regions of interest for scoring and a condensed history algorithm for the rest of the geometry. Since the condensed history code does not correctly follow the tracks of particles below a certain energy threshold, the volume in which the track structure simulation is performed must extend beyond the volume in which scoring is done. The effect of this extended volume on simulation accuracy and performance are discussed, and it is shown that the watch volume must extend beyond the target by a distance equal to the range of the subthreshold electrons. This simulation method is benchmarked against experimental measurements for several radioisotopes and run for a volumetric arc radiotherapy plan. In addition, there is a comparison of the microdosimetric characteristics of two widely used track structure simulations(Geant4-DNA and NOREC), and a discussion of the use of Monte Carlo in the patient specific treatment planning for Stereotactic Body Radiotherapy and Total Body Irradiation.

Text

2013-09-09

Attribution 2.5 Canada

http://hdl.handle.net/2429/45045

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/2.5/ca/