Literature DB >> 12894973

Convolution/superposition using the Monte Carlo method.

Shahid A Naqvi1, Matthew A Earl, David M Shepard.   

Abstract

The convolution/superposition calculations for radiotherapy dose distributions are traditionally performed by convolving polyenergetic energy deposition kernels with TERMA (total energy released per unit mass) precomputed in each voxel of the irradiated phantom. We propose an alternative method in which the TERMA calculation is replaced by random sampling of photon energy, direction and interaction point. Then, a direction is randomly sampled from the angular distribution of the monoenergetic kernel corresponding to the photon energy. The kernel ray is propagated across the phantom, and energy is deposited in each voxel traversed. An important advantage of the explicit sampling of energy is that spectral changes with depth are automatically accounted for. No spectral or kernel hardening corrections are needed. Furthermore, the continuous sampling of photon direction allows us to model sharp changes in fluence, such as those due to collimator tongue-and-groove. The use of explicit photon direction also facilitates modelling of situations where a given voxel is traversed by photons from many directions. Extra-focal radiation, for instance, can therefore be modelled accurately. Our method also allows efficient calculation of a multi-segment/multi-beam IMRT plan by sampling of beam angles and field segments according to their relative weights. For instance, an IMRT plan consisting of seven 14 x 12 cm2 beams with a total of 300 field segments can be computed in 15 min on a single CPU, with 2% statistical fluctuations at the isocentre of the patient's CT phantom divided into 4 x 4 x 4 mm3 voxels. The calculation contains all aperture-specific effects, such as tongue and groove, leaf curvature and head scatter. This contrasts with deterministic methods in which each segment is given equal importance, and the time taken scales with the number of segments. Thus, the Monte Carlo superposition provides a simple, accurate and efficient method for complex radiotherapy dose calculations.

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Year:  2003        PMID: 12894973     DOI: 10.1088/0031-9155/48/14/305

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


  8 in total

1.  Comparing radiation treatments using intensity-modulated beams, multiple arcs, and single arcs.

Authors:  Grace Tang; Matthew A Earl; Shuang Luan; Chao Wang; Majid M Mohiuddin; Cedric X Yu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-04       Impact factor: 7.038

2.  A convolution/superposition method using primary and scatter dose kernels formed for energy bins of X-ray spectra reconstructed as a function of off-axis distance: a theoretical study on 10-MV X-ray dose calculations in thorax-like phantoms.

Authors:  Akira Iwasaki; Shigenobu Kimura; Kohji Sutoh; Kazuo Kamimura; Makoto Sasamori; Fumio Komai; Morio Seino; Singo Terashima; Mamoru Kubota; Junichi Hirota; Yoichiro Hosokawa
Journal:  Radiol Phys Technol       Date:  2011-06-15

3.  EGSnrc application for IMRT planning.

Authors:  Sitti Yani; Ilmi Rizkia; Mohamad Fahdillah Rhani; Mohammad Haekal; Freddy Haryanto
Journal:  Rep Pract Oncol Radiother       Date:  2020-01-22

4.  A two-stage sequential linear programming approach to IMRT dose optimization.

Authors:  Hao H Zhang; Robert R Meyer; Jianzhou Wu; Shahid A Naqvi; Leyuan Shi; Warren D D'Souza
Journal:  Phys Med Biol       Date:  2010-01-14       Impact factor: 3.609

5.  On the quantification of the dosimetric accuracy of collapsed cone convolution superposition (CCCS) algorithm for small lung volumes using IMRT.

Authors:  Oscar I Calvo; Alonso N Gutiérrez; Sotirios Stathakis; Carlos Esquivel; Nikos Papanikolaou
Journal:  J Appl Clin Med Phys       Date:  2012-05-10       Impact factor: 2.102

6.  A Simple Method for 2-D In Vivo Dosimetry by Portal Imaging.

Authors:  Stefano Peca; Derek Wilson Brown; Wendy Lani Smith
Journal:  Technol Cancer Res Treat       Date:  2017-06-06

7.  Optimizing the MLC model parameters for IMRT in the RayStation treatment planning system.

Authors:  Shifeng Chen; Byong Yong Yi; Xiaocheng Yang; Huijun Xu; Karl L Prado; Warren D D'Souza
Journal:  J Appl Clin Med Phys       Date:  2015-09-08       Impact factor: 2.102

8.  A Treatment Planning Method for Better Management of Radiation-Induced Oral Mucositis in Locally Advanced Head and Neck Cancer.

Authors:  Hao Howard Zhang; Warren D D'Souza
Journal:  J Med Phys       Date:  2018 Jan-Mar
  8 in total

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