Literature DB >> 17881793

An efficient framework for photon Monte Carlo treatment planning.

Michael K Fix1, Peter Manser, Daniel Frei, Werner Volken, Roberto Mini, Ernst J Born.   

Abstract

Currently photon Monte Carlo treatment planning (MCTP) for a patient stored in the patient database of a treatment planning system (TPS) can usually only be performed using a cumbersome multi-step procedure where many user interactions are needed. This means automation is needed for usage in clinical routine. In addition, because of the long computing time in MCTP, optimization of the MC calculations is essential. For these purposes a new graphical user interface (GUI)-based photon MC environment has been developed resulting in a very flexible framework. By this means appropriate MC transport methods are assigned to different geometric regions by still benefiting from the features included in the TPS. In order to provide a flexible MC environment, the MC particle transport has been divided into different parts: the source, beam modifiers and the patient. The source part includes the phase-space source, source models and full MC transport through the treatment head. The beam modifier part consists of one module for each beam modifier. To simulate the radiation transport through each individual beam modifier, one out of three full MC transport codes can be selected independently. Additionally, for each beam modifier a simple or an exact geometry can be chosen. Thereby, different complexity levels of radiation transport are applied during the simulation. For the patient dose calculation, two different MC codes are available. A special plug-in in Eclipse providing all necessary information by means of Dicom streams was used to start the developed MC GUI. The implementation of this framework separates the MC transport from the geometry and the modules pass the particles in memory; hence, no files are used as the interface. The implementation is realized for 6 and 15 MV beams of a Varian Clinac 2300 C/D. Several applications demonstrate the usefulness of the framework. Apart from applications dealing with the beam modifiers, two patient cases are shown. Thereby, comparisons are performed between MC calculated dose distributions and those calculated by a pencil beam or the AAA algorithm. Interfacing this flexible and efficient MC environment with Eclipse allows a widespread use for all kinds of investigations from timing and benchmarking studies to clinical patient studies. Additionally, it is possible to add modules keeping the system highly flexible and efficient.

Entities:  

Mesh:

Year:  2007        PMID: 17881793     DOI: 10.1088/0031-9155/52/19/N01

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  5 in total

1.  Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization.

Authors:  Hualiang Zhong; Indrin J Chetty
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

Review 2.  Monte Carlo systems used for treatment planning and dose verification.

Authors:  Lorenzo Brualla; Miguel Rodriguez; Antonio M Lallena
Journal:  Strahlenther Onkol       Date:  2016-11-25       Impact factor: 3.621

3.  Implementation and experimental validation of a robust hybrid direct aperture optimization approach for mixed-beam radiotherapy.

Authors:  Emily Heath; Silvan Mueller; Gian Guyer; Alisha Duetschler; Olgun Elicin; Daniel Aebersold; Michael K Fix; Peter Manser
Journal:  Med Phys       Date:  2021-10-14       Impact factor: 4.506

4.  Development and validation of MCNPX-based Monte Carlo treatment plan verification system.

Authors:  Iraj Jabbari; Shahram Monadi
Journal:  J Med Phys       Date:  2015 Apr-Jun

5.  Development of a Monte Carlo based robustness calculation and evaluation tool.

Authors:  Hannes A Loebner; Werner Volken; Silvan Mueller; Jenny Bertholet; Paul-Henry Mackeprang; Gian Guyer; Daniel M Aebersold; Marco F M Stampanoni; Peter Manser; Michael K Fix
Journal:  Med Phys       Date:  2022-05-04       Impact factor: 4.506

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.