Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-22T04:05:55.593Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterising intensity-modulated radiation therapy (IMRT) software following upgrades in a commercial treatment planning system

Published online by Cambridge University Press:  12 November 2010

Connor McGarry ,
Monica O’Toole  and
Vivian Cosgrove
Connor McGarry*
Affiliation:
Radiotherapy Physics Department, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, Northern Ireland, UK
Monica O’Toole
Affiliation:
Radiotherapy Physics Department, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
Vivian Cosgrove
Affiliation:
Radiotherapy Physics Department, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
*
Correspondence to: C.K. McGarry, Radiotherapy Physics Department, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

When upgrading treatment planning software, it is important to understand and characterise any changes that may have been made to the system. This includes inverse treatment planning and dose optimisation software used for intensity-modulated radiation therapy (IMRT). A systematic and practical approach to characterising dose optimisation software following upgrades is presented based on a planning study of six IMRT prostate cases using the commercial treatment planning system Oncentra Masterplan (OMP). Upgrades included general changes in the fluence to multileaf collimator (MLC) segmentation algorithm, a change from a two-step to a one-step optimisation method and an upgrade of the dose calculation algorithm. Post upgrade changes in plan parameters such as calculation times, monitor units, segments and target doses were analysed. A 32% reduction in total calculated monitor units was observed following the general software upgrade. A smaller 12% reduction was observed when using the optional one-step optimisation method rather than a two-step process using a classic dose calculation algorithm. An increase in monitor units of approximately 12% was observed when changing to an enhanced dose calculation algorithm. The enhanced dose calculation algorithm accounted for MLC type, leakage and source size unlike the previous classic dose calculation algorithm. Differences in dose to volumes between fluence segmentation and final dose calculation varied between versions. These differences were found to be minimal for the most recent treatment planning system version. Repeatability tests revealed a more effective use of the system. The characterisation of the effects of treatment planning software upgrades allowed a better appreciation of IMRT planning and delivery attributes. Although this work is based on one commercial inverse treatment planning system, it would be easily transferable to other systems as the underlying system principles are the same.

Keywords

Inverse planningIMRToptimisationquality assurance
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 9 , Issue 4 , December 2010 , pp. 209 - 221
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fogliata, A, Nicolini, G, Alber, M, Asell, M, Clivio, A, Dobler, B, Larsson, M, Lohr, F, Lorenz, F, Muzik, J, Polednik, M, Vanetti, E, Wolff, D, Wyttenbach, R, Cozzi, L. On the performances of different IMRT Treatment Planning Systems for selected paediatric cases. Radiat Oncol 2007; 2: 7.CrossRefGoogle ScholarPubMed
Seco, J, Clark, CH, Evans, PM, Webb, S. A quantitative study of IMRT delivery effects in commercial planning systems for the case of oesophagus and prostate tumours. Br J Radiol 2006; 79: 401–408.CrossRefGoogle ScholarPubMed
Dobler, B, Pohl, F, Bogner, L, Koelbl, O. Comparison of direct machine parameter optimization versus fluence optimization with sequential sequencing in IMRT of hypopharyngeal carcinoma. Radiat Oncol 2007; 2: 33.CrossRefGoogle ScholarPubMed
Shepard, DM, Earl, MA, Li, XA, Naqvi, S, Yu, C. Direct aperture optimization: a turnkey solution for step-and-shoot IMRT. Med Phys 2002; 29: 1007–1018.CrossRefGoogle ScholarPubMed
Sun, X, Xia, P. A new smoothing procedure to reduce delivery segments for static MLC-based IMRT planning. Med Phys 2004; 31: 1158–1165.CrossRefGoogle ScholarPubMed
Giorgia, N, Antonella, F, Eugenio, V, Alessandro, C, Filippo, A, Luca, C. What is an acceptably smoothed fluence? Dosimetric and delivery considerations for dynamic sliding window IMRT. Radiat Oncol 2007; 2:42.CrossRefGoogle ScholarPubMed
Ludlum, E, Xia, P. Comparison of IMRT planning with two-step and one-step optimization: a way to simplify IMRT. Phys Med Biol 2008; 53: 807–821.CrossRefGoogle ScholarPubMed
Jones, S, Williams, M. Clinical evaluation of direct aperture optimization when applied to head-and-neck IMRT. Med Dosim 2008; 33: 86–92.CrossRefGoogle ScholarPubMed
Siebers, JV, Lauterbach, M, Tong, S, Wu, Q, Mohan, R. Reducing dose calculation time for accurate iterative IMRT planning. Med Phys 2002; 29: 231–237.CrossRefGoogle ScholarPubMed
Hartford, AC, Palisca, MG, Eichler, TJ, Beyer, DC, Devineni, VR, Ibbott, GS, Kavanagh, B, Kent, JS, Rosenthal, SA, Schultz, CJ, Tripuraneni, P, Gaspar, LE. American Society for Therapeutic Radiology and Oncology; American College of Radiology. American Society for Therapeutic Radiology and Oncology (ASTRO) and American College of Radiology (ACR) Practice Guidelines for Intensity-Modulated Radiation Therapy (IMRT). Int J Radiat Oncol Biol Phys 2009; 73: 9–14.CrossRefGoogle ScholarPubMed
Galvin, JM, Ezzell, G, Eisbrauch, A, Yu, C, Butler, B, Xiao, Y, Rosen, I, Rosenman, J, Sharpe, M, Xing, L, Xia, P, Lomax, T, Low, DA, Palta, J. American Society for Therapeutic Radiology and Oncology; American Association of Physicists in Medicine. Implementing IMRT in clinical practice: a joint document of the American Society for Therapeutic Radiology and Oncology and the American Association of Physicists in Medicine. Int J Radiat Oncol Biol Phys 2004; 58: 1616–1634.CrossRefGoogle Scholar
Ahnesjo, A. Calculation II – Advanced kernel models in Photon Treatment Planning. Radiother Oncol 2007; 84(Suppl 1):S2Google Scholar
Kuperman, VY, Ventura, AM, Sommerfeldt, M. Effect of radiation protraction in intensity-modulated radiation therapy with direct aperture optimization: a phantom study. Phys Med Biol 2008; 53: 3279–3292.CrossRefGoogle ScholarPubMed