IMRT head and neck treatment planning with a commercially available Monte Carlo based planning system

Boudreau, Chantal; Heath, Emily; Seuntjens, J; Ballivy, Olivier; Parker, William

doi:10.1088/0031-9155/50/5/012

Cited by 29 publications

(25 citation statements)

References 26 publications

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“…It has been reported in several publications 5,7,8,10,12,18,[38][39][40] that the differences between measured/MC-derived doses and doses calculated by correction/PB-based algorithms are larger than the differences between measured/MC doses and SC doses. In other words, the DPEs of PB/correction-based algorithms are larger than the DPEs of SC algorithms.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

Mihaylov

Siebers

2008

Med. Phys.

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The purpose of this study is to evaluate dose prediction errors ͑DPEs͒ and optimization convergence errors ͑OCEs͒ resulting from use of a superposition/convolution dose calculation algorithm in deliverable intensity-modulated radiation therapy ͑IMRT͒ optimization for head-and-neck ͑HN͒ patients. Thirteen HN IMRT patient plans were retrospectively reoptimized. The IMRT optimization was performed in three sequential steps: ͑1͒ fast optimization in which an initial nondeliverable IMRT solution was achieved and then converted to multileaf collimator ͑MLC͒ leaf sequences; ͑2͒ mixed deliverable optimization that used a Monte Carlo ͑MC͒ algorithm to account for the incident photon fluence modulation by the MLC, whereas a superposition/convolution ͑SC͒ dose calculation algorithm was utilized for the patient dose calculations; and ͑3͒ MC deliverable-based optimization in which both fluence and patient dose calculations were performed with a MC algorithm. DPEs of the mixed method were quantified by evaluating the differences between the mixed optimization SC dose result and a MC dose recalculation of the mixed optimization solution. OCEs of the mixed method were quantified by evaluating the differences between the MC recalculation of the mixed optimization solution and the final MC optimization solution. The results were analyzed through dose volume indices derived from the cumulative dose-volume histograms for selected anatomic structures. Statistical equivalence tests were used to determine the significance of the DPEs and the OCEs. Furthermore, a correlation analysis between DPEs and OCEs was performed. The evaluated DPEs were within Ϯ2.8% while the OCEs were within 5.5%, indicating that OCEs can be clinically significant even when DPEs are clinically insignificant. The full MC-dose-based optimization reduced normal tissue dose by as much as 8.5% compared with the mixed-method optimization results. The DPEs and the OCEs in the targets had correlation coefficients greater than 0.71, and there was no correlation for the organs at risk. Because full MC-based optimization results in lower normal tissue doses, this method proves advantageous for HN IMRT optimization.

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Section: Discussionmentioning

confidence: 99%

Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

Mihaylov

Siebers

2008

Med. Phys.

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“…Air cavities were removed manually from the PTV contour ͑PTV-air͒ for DVH calculation in order to avoid deviations introduced by poor statistics of MC calculations in air. 35 …”

Section: Ethmoid Tumormentioning

confidence: 99%

Monte Carlo evaluation of the AAA treatment planning algorithm in a heterogeneous multilayer phantom and IMRT clinical treatments for an Elekta SL25 linear accelerator

et al. 2007

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The Anisotropic Analytical Algorithm (AAA) is a new pencil beam convolution/superposition algorithm proposed by Varian for photon dose calculations. The configuration of AAA depends on linear accelerator design and specifications. The purpose of this study was to investigate the accuracy of AAA for an Elekta SL25 linear accelerator for small fields and intensity modulated radiation therapy (IMRT) treatments in inhomogeneous media. The accuracy of AAA was evaluated in two studies. First, AAA was compared both with Monte Carlo (MC) and the measurements in an inhomogeneous phantom simulating lung equivalent tissues and bone ribs. The algorithm was tested under lateral electronic disequilibrium conditions, using small fields (2 x 2 cm(2)). Good agreement was generally achieved for depth dose and profiles, with deviations generally below 3% in lung inhomogeneities and below 5% at interfaces. However, the effects of attenuation and scattering close to the bone ribs were not fully taken into account by AAA, and small inhomogeneities may lead to planning errors. Second, AAA and MC were compared for IMRT plans in clinical conditions, i.e., dose calculations in a computed tomography scan of a patient. One ethmoid tumor, one orophaxynx and two lung tumors are presented in this paper. Small differences were found between the dose volume histograms. For instance, a 1.7% difference for the mean planning target volume dose was obtained for the ethmoid case. Since better agreement was achieved for the same plans but in homogeneous conditions, these differences must be attributed to the handling of inhomogeneities by AAA. Therefore, inherent assumptions of the algorithm, principally the assumption of independent depth and lateral directions in the scaling of the kernels, were slightly influencing AAA's validity in inhomogeneities. However, AAA showed a good accuracy overall and a great ability to handle small fields in inhomogeneous media compared to other pencil beam convolution algorithms.

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“…However, with respect to the development of computer technology and variance reduction techniques, the MC method is becoming a practical approach in dose calculations for three-dimensional (3D) conventional radiotherapy and it has been recently applied in conformal radiation therapy techniques such as intensity modulated radiation therapy, microbeam radiation therapy and proton beam therapy. [456]…”

Section: Introductionmentioning

confidence: 99%

A Comparison between GATE and MCNPX monte carlo codes in simulation of medical linear accelerator

et al. 2014

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Radiotherapy dose calculations can be evaluated by Monte Carlo (MC) simulations with acceptable accuracy for dose prediction in complicated treatment plans. In this work, Standard, Livermore and Penelope electromagnetic (EM) physics packages of GEANT4 application for tomographic emission (GATE) 6.1 were compared versus Monte Carlo N-Particle eXtended (MCNPX) 2.6 in simulation of 6 MV photon Linac. To do this, similar geometry was used for the two codes. The reference values of percentage depth dose (PDD) and beam profiles were obtained using a 6 MV Elekta Compact linear accelerator, Scanditronix water phantom and diode detectors. No significant deviations were found in PDD, dose profile, energy spectrum, radial mean energy and photon radial distribution, which were calculated by Standard and Livermore EM models and MCNPX, respectively. Nevertheless, the Penelope model showed an extreme difference. Statistical uncertainty in all the simulations was <1%, namely 0.51%, 0.27%, 0.27% and 0.29% for PDDs of 10 cm2× 10 cm2 filed size, for MCNPX, Standard, Livermore and Penelope models, respectively. Differences between spectra in various regions, in radial mean energy and in photon radial distribution were due to different cross section and stopping power data and not the same simulation of physics processes of MCNPX and three EM models. For example, in the Standard model, the photoelectron direction was sampled from the Gavrila-Sauter distribution, but the photoelectron moved in the same direction of the incident photons in the photoelectric process of Livermore and Penelope models. Using the same primary electron beam, the Standard and Livermore EM models of GATE and MCNPX showed similar output, but re-tuning of primary electron beam is needed for the Penelope model.

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IMRT head and neck treatment planning with a commercially available Monte Carlo based planning system

Cited by 29 publications

References 26 publications

Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

Monte Carlo evaluation of the AAA treatment planning algorithm in a heterogeneous multilayer phantom and IMRT clinical treatments for an Elekta SL25 linear accelerator

A Comparison between GATE and MCNPX monte carlo codes in simulation of medical linear accelerator

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