Literature DB >> 19550004

Dose to water versus dose to medium in proton beam therapy.

Harald Paganetti1.   

Abstract

Dose in radiation therapy is traditionally reported as the water-equivalent dose, or dose to water. Monte Carlo dose calculations report dose to medium and thus a methodology is needed to convert dose to medium into dose to water (or vice versa) for comparison of Monte Carlo results with results from planning systems. This paper describes the development of a formalism to convert dose to medium into dose to water for proton fields when simulating the dose with Monte Carlo techniques. The conversion is based on relative stopping power but also considers energy transferred via nuclear interactions. The influence of different interaction mechanisms of proton beams (electromagnetic versus nuclear) is demonstrated. Further, an approximate method for converting doses retroactively is presented. Based on the outlined formalism, five proton therapy patients with a total of 33 fields were analyzed. Dose distributions, dose volume histograms and absolute doses to assess the clinical significance of differences between dose to medium and dose to water are presented. We found that the difference between the two dose reporting definitions can be up to 10% for high CT numbers if analyzing the mean dose to the target. The difference is clinically insignificant for soft tissues. For the structures analyzed, the mean dose to water could be converted to dose to medium by applying a correction factor increasing linearly with increasing average CT number in the volume. We determined that an approximate conversion method, done retroactively with an energy-independent stopping power ratio and without considering nuclear interaction events separately (as compared to on-the-fly conversion during simulation), is sufficiently accurate to compute mean doses. It is insufficient, however, when analyzing the beam range. For proton beams stopping in bony anatomy, the predicted beam range can differ by 2-3 mm when comparing dose to tissue and dose to water.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19550004     DOI: 10.1088/0031-9155/54/14/004

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


  10 in total

1.  Dosimetric accuracy of planning and delivering small proton therapy fields.

Authors:  Bryan Bednarz; Juliane Daartz; Harald Paganetti
Journal:  Phys Med Biol       Date:  2010-11-19       Impact factor: 3.609

2.  Intensity modulated proton therapy.

Authors:  H M Kooy; C Grassberger
Journal:  Br J Radiol       Date:  2015-05-27       Impact factor: 3.039

3.  Evaluation of energy deposition and secondary particle production in proton therapy of brain using a slab head phantom.

Authors:  Sayyed Bijan Jia; Mohammad Hadi Hadizadeh; Ali Asghar Mowlavi; Mahdy Ebrahimi Loushab
Journal:  Rep Pract Oncol Radiother       Date:  2014-05-01

4.  Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy.

Authors:  Nora Hünemohr; Harald Paganetti; Steffen Greilich; Oliver Jäkel; Joao Seco
Journal:  Med Phys       Date:  2014-06       Impact factor: 4.071

5.  Linear energy transfer-guided optimization in intensity modulated proton therapy: feasibility study and clinical potential.

Authors:  Drosoula Giantsoudi; Clemens Grassberger; David Craft; Andrzej Niemierko; Alexei Trofimov; Harald Paganetti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2013-06-19       Impact factor: 7.038

6.  Dosimetric accuracy of proton therapy for chordoma patients with titanium implants.

Authors:  Joost M Verburg; Joao Seco
Journal:  Med Phys       Date:  2013-07       Impact factor: 4.071

7.  Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.

Authors:  Drosoula Giantsoudi; Jan Schuemann; Xun Jia; Stephen Dowdell; Steve Jiang; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-02-26       Impact factor: 3.609

Review 8.  Range uncertainties in proton therapy and the role of Monte Carlo simulations.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

9.  Automated Monte-Carlo re-calculation of proton therapy plans using Geant4/Gate: implementation and comparison to plan-specific quality assurance measurements.

Authors:  Adam H Aitkenhead; Peter Sitch; Jenny C Richardson; Carla Winterhalter; Imran Patel; Ranald I Mackay
Journal:  Br J Radiol       Date:  2020-07-29       Impact factor: 3.039

10.  A simplified methodology to produce Monte Carlo dose distributions in proton therapy.

Authors:  Chris Beltran; Yingcui Jia; Roelf Slopsema; Daniel Yeung; Zuofeng Li
Journal:  J Appl Clin Med Phys       Date:  2014-07-08       Impact factor: 2.102

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.