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The influence of air cavities within the PTV on Monte Carlo-based IMRT optimization

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Abstract
ntegrating Monte Carlo calculated dose distributions into an iterative aperture-based IMRT optimization process can improve the final treatment plan. However, the influence of large air cavities in the planning target volume (PTV) on the outcome of the optimization process should not be underestimated. To study this influence, the treatment plan of an ethmoid sinus cancer patient, which has large air cavities included in the PTV, is iteratively optimized in two different situations, namely when the large air cavities are included in the PTV and when these air cavities are excluded from the PTV. Two optimization methods were applied to integrate the Monte Carlo calculated dose distributions into the optimization process, namely the 'Correction - method' and the 'Per Segment - method'. The 'Correction - method' takes the Monte Carlo calculated global dose distribution into account in the optimization process by means of a correction matrix, which is in fact a dose distribution that is equal to the difference between the Monte Carlo calculated global dose distribution and the global dose distribution calculated by a conventional dose calculation algorithm. The 'Per Segment - method' uses directly the Monte Carlo calculated dose distributions of the individual segments in the optimization process. Both methods tend to converge whether or not large air cavities are excluded from the PTV during the optimization process. However, the 'Per Segment - method' performs better than the 'Correction - method' in both situations and the 'Per Segment - method' in the case where the large air cavities are excluded from the PTV leads to a better treatment plan then when these air cavities are included. Therefore we advise to exclude large air cavities and to apply the 'Per Segment - method' to integrate the Monte Carlo dose calculations into an iterative aperture-based optimization process. Nevertheless, the 'Correction method' provides a good alternative in the case when the external dose engine is not able to generate individual dose distributions for the individual segments.
Keywords
Applied, Nuclear Medicine & Medical Imaging, Radiology, Physics

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Chicago
De Smedt, Bart, Barbara Vanderstraeten, Nick Reynaert, Werner De Gersem, Wilfried De Neve, and Hubert Thierens. 2007. “The Influence of Air Cavities Within the PTV on Monte Carlo-based IMRT Optimization.” In Journal of Physics Conference Series, 74:U26–U33. Bristol, England: IOP PUBLISHING LTD.
APA
De Smedt, B., Vanderstraeten, B., Reynaert, N., De Gersem, W., De Neve, W., & Thierens, H. (2007). The influence of air cavities within the PTV on Monte Carlo-based IMRT optimization. JOURNAL OF PHYSICS CONFERENCE SERIES (Vol. 74, pp. U26–U33). Presented at the 1st European Workshop on Monte Carlo Treatment Planning, Bristol, England: IOP PUBLISHING LTD.
Vancouver
1.
De Smedt B, Vanderstraeten B, Reynaert N, De Gersem W, De Neve W, Thierens H. The influence of air cavities within the PTV on Monte Carlo-based IMRT optimization. JOURNAL OF PHYSICS CONFERENCE SERIES. Bristol, England: IOP PUBLISHING LTD; 2007. p. U26–U33.
MLA
De Smedt, Bart, Barbara Vanderstraeten, Nick Reynaert, et al. “The Influence of Air Cavities Within the PTV on Monte Carlo-based IMRT Optimization.” Journal of Physics Conference Series. Vol. 74. Bristol, England: IOP PUBLISHING LTD, 2007. U26–U33. Print.
@inproceedings{531200,
  abstract     = {ntegrating Monte Carlo calculated dose distributions into an iterative aperture-based IMRT optimization process can improve the final treatment plan. However, the influence of large air cavities in the planning target volume (PTV) on the outcome of the optimization process should not be underestimated. To study this influence, the treatment plan of an ethmoid sinus cancer patient, which has large air cavities included in the PTV, is iteratively optimized in two different situations, namely when the large air cavities are included in the PTV and when these air cavities are excluded from the PTV. Two optimization methods were applied to integrate the Monte Carlo calculated dose distributions into the optimization process, namely the 'Correction - method' and the 'Per Segment - method'. The 'Correction - method' takes the Monte Carlo calculated global dose distribution into account in the optimization process by means of a correction matrix, which is in fact a dose distribution that is equal to the difference between the Monte Carlo calculated global dose distribution and the global dose distribution calculated by a conventional dose calculation algorithm. The 'Per Segment - method' uses directly the Monte Carlo calculated dose distributions of the individual segments in the optimization process. Both methods tend to converge whether or not large air cavities are excluded from the PTV during the optimization process. However, the 'Per Segment - method' performs better than the 'Correction - method' in both situations and the 'Per Segment - method' in the case where the large air cavities are excluded from the PTV leads to a better treatment plan then when these air cavities are included. Therefore we advise to exclude large air cavities and to apply the 'Per Segment - method' to integrate the Monte Carlo dose calculations into an iterative aperture-based optimization process. Nevertheless, the 'Correction method' provides a good alternative in the case when the external dose engine is not able to generate individual dose distributions for the individual segments.},
  author       = {De Smedt, Bart and Vanderstraeten, Barbara and Reynaert, Nick and De Gersem, Werner and De Neve, Wilfried and Thierens, Hubert},
  booktitle    = {JOURNAL OF PHYSICS CONFERENCE SERIES},
  issn         = {1742-6588},
  keyword      = {Applied,Nuclear Medicine \& Medical Imaging,Radiology,Physics},
  language     = {eng},
  location     = {Gent, BELGIUM},
  pages        = {U26--U33},
  publisher    = {IOP PUBLISHING LTD},
  title        = {The influence of air cavities within the PTV on Monte Carlo-based IMRT optimization},
  url          = {http://dx.doi.org/10.1088/1742-6596/74/1/021003},
  volume       = {74},
  year         = {2007},
}

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