Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1.R. Li and L. Xing, “An adaptive planning strategy for station parameter optimized radiation therapy (SPORT): Segmentally boosted VMAT,” Med. Phys. 40, 050701 (9pp.) (2013).
2.L. Xing, M. H. Phillips, and C. G. Orton, “Point/counterpoint. DASSIM-RT is likely to become the method of choice over conventional IMRT and VMAT for delivery of highly conformal radiotherapy,” Med. Phys. 40, 020601 (3pp.) (2013).
3.R. Li, L. Xing, K. C. Horst, and K. Bush, “Nonisocentric treatment strategy for breast radiation therapy: A proof of concept study,” Int. J. Radiat. Oncol., Biol., Phys. 88, 920926 (2014).
4.K. Otto, “Volumetric modulated arc therapy: IMRT in a single gantry arc,” Med. Phys. 35, 310317 (2008).
5.C. X. Yu, “Intensity-modulated arc therapy with dynamic multileaf collimation: An alternative to tomotherapy,” Phys. Med. Biol. 40, 14351449 (1995).
6.C. X. Yu and G. Tang, “Intensity-modulated arc therapy: Principles, technologies and clinical implementation,” Phys. Med. Biol. 56, R31R54 (2011).
7.A. Boyer et al., “Theoretical considerations of monitor unit calculations for intensity modulated beam treatment planning,” Med. Phys. 26, 187195 (1999).
8.L. Xing, Y. Chen, G. Luxton, J. G. Li, and A. L. Boyer, “Monitor unit calculation for an intensity modulated photon field by a simple scatter-summation algorithm,” Phys. Med. Biol. 45, N1N7 (2000).
9.Y. Yang et al., “Independent dosimetric calculation with inclusion of head scatter and MLC transmission for IMRT,” Med. Phys. 30, 29372947 (2003).
10.G. A. Ezzell et al., “Guidance document on delivery, treatment planning, and clinical implementation of IMRT: Report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee,” Med. Phys. 30, 20892115 (2003).
11.O. Pisaturo, R. Moeckli, R. O. Mirimanoff, and F. O. Bochud, “A Monte Carlo-based procedure for independent monitor unit calculation in IMRT treatment plans,” Phys. Med. Biol. 54, 42994310 (2009).
12.R. L. Stern et al., “Verification of monitor unit calculations for non-IMRT clinical radiotherapy: Report of AAPM Task Group 114,” Med. Phys. 38, 504530 (2011).
13.D. A. Low, J. M. Moran, J. F. Dempsey, L. Dong, and M. Oldham, “Dosimetry tools and techniques for IMRT,” Med. Phys. 38, 13131338 (2011).
14.L. Xing et al., “Dosimetric verification of a commercial inverse treatment planning system,” Phys. Med. Biol. 44, 463478 (1999).
15.L. Masi, F. Casamassima, R. Doro, and P. Francescon, “Quality assurance of volumetric modulated arc therapy: Evaluation and comparison of different dosimetric systems,” Med. Phys. 38, 612621 (2011).
16.J. L. Bedford, Y. K. Lee, P. Wai, C. P. South, and A. P. Warrington, “Evaluation of the Delta4 phantom for IMRT and VMAT verification,” Phys. Med. Biol. 54, N167N176 (2009).
17.R. Boggula et al., “Experimental validation of a commercial 3D dose verification system for intensity-modulated arc therapies,” Phys. Med. Biol. 55, 56195633 (2010).
18.D. Letourneau, J. Publicover, J. Kozelka, D. J. Moseley, and D. A. Jaffray, “Novel dosimetric phantom for quality assurance of volumetric modulated arc therapy,” Med. Phys. 36, 18131821 (2009).
19.A. Manikandan, B. Sarkar, R. Holla, T. R. Vivek, and N. Sujatha, “Quality assurance of dynamic parameters in volumetric modulated arc therapy,” Br. J. Radiol. 85, 10021010 (2012).
20.L. Wang, K. N. Kielar, E. Mok, A. Hsu, S. Dieterich, and L. Xing, “An end-to-end examination of geometric accuracy of IGRT using a new digital accelerator equipped with onboard imaging system,” Phys. Med. Biol. 57, 757769 (2012).
21.J. Qian, L. Xing, W. Liu, and G. Luxton, “Dose verification for respiratory-gated volumetric modulated arc therapy,” Phys. Med. Biol. 56, 48274838 (2011).
22.C. C. Ling, P. Zhang, Y. Archambault, J. Bocanek, G. Tang, and T. Losasso, “Commissioning and quality assurance of RapidArc radiotherapy delivery system,” Int. J. Radiat. Oncol., Biol., Phys. 72, 575581 (2008).
23.H. Zhen, B. E. Nelms, and W. A. Tome, “Moving from gamma passing rates to patient DVH-based QA metrics in pretreatment dose QA,” Med. Phys. 38, 54775489 (2011).
24.I. Kawrakow, M. Fippel, and K. Friedrich, “3D electron dose calculation using a voxel based Monte Carlo algorithm (VMC),” Med. Phys. 23, 445457 (1996).
25.K. Bush, S. F. Zavgorodni, and W. A. Beckham, “Azimuthal particle redistribution for the reduction of latent phase–space variance in Monte Carlo simulations,” Phys. Med. Biol. 52, 43454360 (2007).
26.K. Bush, R. Townson, and S. Zavgorodni, “Monte Carlo simulation of RapidArc radiotherapy delivery,” Phys. Med. Biol. 53, N359N370 (2008).
27.D. W. O. Rogers, B. A. Faddegon, G. X. Ding, C. M. Ma, J. We, and T. R. Mackie, “Beam—A Monte-Carlo code to simulate radiotherapy treatment units,” Med. Phys. 22, 503524 (1995).
28.E. Schreibmann and L. Xing, “Narrow band deformable registration of prostate magnetic resonance imaging, magnetic resonance spectroscopic imaging, and computed tomography studies,” Int. J. Radiat. Oncol., Biol., Phys. 62, 595605 (2005).
29.M. Hussein, P. Rowshanfarzad, M. A. Ebert, A. Nisbet, and C. H. Clark, “A comparison of the gamma index analysis in various commercial IMRT/VMAT QA systems,” Radiother. Oncol. 109, 370376 (2013).
30.G. Pratx and L. Xing, “Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce,” J. Biomed. Opt. 16, 125003 (2011).
31.H. Wang, Y. Ma, G. Pratx, and L. Xing, “Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure,” Phys. Med. Biol. 56, N175N181 (2011).

Data & Media loading...


Article metrics loading...



Dose and monitor units (MUs) represent two important facets of a radiation therapy treatment. In current practice, verification of a treatment plan is commonly done in dose domain, in which a phantom measurement or forward dose calculation is performed to examine the dosimetric accuracy and the MU settings of a given treatment plan. While it is desirable to verify directly the MU settings, a computational framework for obtaining the MU values from a known dose distribution has yet to be developed. This work presents a strategy to calculate independently the MUs from a given dose distribution of volumetric modulated arc therapy (VMAT) and station parameter optimized radiation therapy (SPORT).

The dose at a point can be expressed as a sum of contributions from all the station points (or control points). This relationship forms the basis of the proposed MU verification technique. To proceed, the authors first obtain the matrix elements which characterize the dosimetric contribution of the involved station points by computing the doses at a series of voxels, typically on the prescription surface of the VMAT/SPORT treatment plan, with unit MU setting for all the station points. An in-house Monte Carlo (MC) software is used for the dose matrix calculation. The MUs of the station points are then derived by minimizing the least-squares difference between doses computed by the treatment planning system (TPS) and that of the MC for the selected set of voxels on the prescription surface. The technique is applied to 16 clinical cases with a variety of energies, disease sites, and TPS dose calculation algorithms.

For all plans except the lung cases with large tissue density inhomogeneity, the independently computed MUs agree with that of TPS to within 2.7% for all the station points. In the dose domain, no significant difference between the MC and Eclipse Anisotropic Analytical Algorithm (AAA) dose distribution is found in terms of isodose contours, dose profiles, gamma index, and dose volume histogram (DVH) for these cases. For the lung cases, the MC-calculated MUs differ significantly from that of the treatment plan computed using AAA. However, the discrepancies are reduced to within 3% when the TPS dose calculation algorithm is switched to a transport equation-based technique (Acuros™). Comparison in the dose domain between the MC and Eclipse AAA/Acuros calculation yields conclusion consistent with the MU calculation.

A computational framework relating the MU and dose domains has been established. The framework does not only enable them to verify the MU values of the involved station points of a VMAT plan directly in the MU domain but also provide a much needed mechanism to adaptively modify the MU values of the station points in accordance to a specific change in the dose domain.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd