An efficient framework for photon Monte Carlo treatment planning

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.

[1]  M. Fix,et al.  Implementation of a brachytherapy Ir-source in an in-house system and comparison of simulation results with EGSnrc, VMC++ and PIN , 2007 .

[2]  W. Beckham,et al.  Modelling an extreme water-lung interface using a single pencil beam algorithm and the Monte Carlo method. , 2004, Physics in medicine and biology.

[3]  R Mohan,et al.  A method for determining multileaf collimator transmission and scatter for dynamic intensity modulated radiotherapy. , 2000, Medical physics.

[4]  Joseph O Deasy,et al.  CERR: a computational environment for radiotherapy research. , 2003, Medical physics.

[5]  M. Fix,et al.  A multiple source model for 6 MV photon beam dose calculations using Monte Carlo. , 2001, Physics in medicine and biology.

[6]  J. Seco,et al.  Head-and-neck IMRT treatments assessed with a Monte Carlo dose calculation engine , 2005, Physics in medicine and biology.

[7]  D. Sheikh-Bagheri,et al.  Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters. , 2002, Medical physics.

[8]  Monte Carlo simulation of a dynamic MLC: implementation and applications. , 2001, Zeitschrift fur medizinische Physik.

[9]  Patrick N. McDermott,et al.  Dose calculation accuracy of lung planning with a commercial IMRT treatment planning system , 2003, Journal of applied clinical medical physics.

[10]  I. Kawrakow,et al.  Large efficiency improvements in BEAMnrc using directional bremsstrahlung splitting. , 2004, Medical physics.

[11]  E. Heath,et al.  IMRT head and neck treatment planning with a commercially available Monte Carlo based planning system , 2005, Physics in medicine and biology.

[12]  G. Ding.  Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18 MV photon beams: results of monte carlo simulations for a varian 2100EX accelerator. , 2002, Physics in medicine and biology.

[13]  Barbara Vanderstraeten,et al.  Accuracy of patient dose calculation for lung IMRT: A comparison of Monte Carlo, convolution/superposition, and pencil beam computations. , 2006, Medical physics.

[14]  D W O Rogers,et al.  Monte Carlo calculation of nine megavoltage photon beam spectra using the BEAM code. , 2002, Medical physics.

[15]  H. Thierens,et al.  Comparison of dose-volume histograms of IMRT treatment plans for ethmoid sinus cancer computed by advanced treatment planning systems including Monte Carlo. , 2006, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  J Seuntjens,et al.  MMCTP: a radiotherapy research environment for Monte Carlo and patient-specific treatment planning. , 2007, Physics in medicine and biology.

[17]  T. Mackie,et al.  Modeling photon output caused by backscattered radiation into the monitor chamber from collimator jaws using a Monte Carlo technique. , 2000, Medical physics.

[18]  E. Yorke,et al.  Monte Carlo evaluation of 6 MV intensity modulated radiotherapy plans for head and neck and lung treatments. , 2002, Medical physics.

[19]  R. Mohan,et al.  Comparison of EGS4 and MCNP4b Monte Carlo codes for generation of photon phase space distributions for a Varian 2100C. , 1999, Physics in medicine and biology.

[20]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .

[21]  Qiuwen Wu,et al.  Monte Carlo-based dosimetry of head-and-neck patients treated with SIB-IMRT. , 2006, International journal of radiation oncology, biology, physics.

[22]  Joao Seco,et al.  Conversion of CT numbers into tissue parameters for Monte Carlo dose calculations: a multi-centre study , 2007, Physics in medicine and biology.

[23]  M. Fix,et al.  Optimizing portal dose calculation for an amorphous silicon detector using Swiss Monte Carlo Plan , 2007 .

[24]  R. Mohan,et al.  Determining the incident electron fluence for Monte Carlo-based photon treatment planning using a standard measured data set. , 2003, Medical physics.

[25]  Steve Vinoski,et al.  Advanced CORBA® Programming with C++ , 1999 .

[26]  C. Ling,et al.  Physical and dosimetric aspects of a multileaf collimation system used in the dynamic mode for implementing intensity modulated radiotherapy. , 1998, Medical physics.

[27]  P. Keall,et al.  Monte Carlo source model for photon beam radiotherapy: photon source characteristics. , 2004, Medical physics.

[28]  Michael K Fix,et al.  Photon-beam subsource sensitivity to the initial electron-beam parameters. , 2005, Medical physics.

[29]  R. Mohan,et al.  The impact of electron transport on the accuracy of computed dose. , 2000, Medical physics.

[30]  C. Ma,et al.  Clinical implementation of a Monte Carlo treatment planning system. , 1999, Medical physics.

[31]  W. Ulmer,et al.  A 3D photon superposition/convolution algorithm and its foundation on results of Monte Carlo calculations , 2005, Physics in medicine and biology.

[32]  P. Carrasco,et al.  Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium. , 2004, Medical physics.