Generation and application of Monte Carlo calculated beamlet dose distributions in radiation therapy

• Main Author: Bush, Karl Kenneth
• Other Authors: Popescu, Ioan Antoniu
• Language: English; en
• Published: 2009
• Subjects: medical physics; radiotherapy; UVic Subject Index::Sciences and Engineering::Health Sciences::Radiology; UVic Subject Index::Sciences and Engineering::Physics
• Online Access: http://hdl.handle.net/1828/1824

The use of beamlets as a dose calculation tool in Intensity Modulated Radiation Therapy (IMRT) treatment planning is widespread and well documented. A beamlet can simply be defined as the contribution of radiation passing through a particular geometrically defined subdivision of a given linear accelerator's emerging radiation field. The most common classes of algorithms used today to calculate the dose distributions deposited by beamlets are the pencil beam convolution and col-lapsed cone classes of algorithms. Using BEAMnrc [1], a Monte Carlo (MC) based radiation transport simulation software package, this thesis presents a novel method of calculating MC beamlet dose distributions with a level of accuracy not achievable using the above analytic dose calculation methods. In a first application, the MC beamlet dose distributions generated in this thesis are used to fine-tune the output of the MC or "virtual" linear accelerator from which they are produced. This is achieved through the adjustment of individual beamlet weights to align the output of the virtual accelerator to the experimentally measured output of the modeled accelerator in water. In a second application, MC beamlets are used to derive corrections to particular Multileaf Collimator (MLC) leaf sequences of IMRT treatment plans that have been miscalculated by a convolution-based dose calculation algorithm. These calculation inaccuracies (up to as much as 15%) arise due to the well known fact that convolution-based algorithms do not accurately model dose deposition in inhomoge¬neous media, such as lung [2] [3] [4]. In a final application, the MC beamlet generation method described in this thesis is implemented into a direct aperture optimization (DAO) algorithm. The implementation of MC beamlet generation in DAO forms the basis for a purely MC based inverse treatment planning system.