Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T12:21:11.522Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of monitor unit through analytical method for dynamic IMRT using control points as an effective parameter

Published online by Cambridge University Press:  02 March 2021

M. Athiyaman Open the ORCID record for M. Athiyaman [Opens in a new window] ,
A. Hemalatha Open the ORCID record for A. Hemalatha [Opens in a new window] ,
Arun Chougule Open the ORCID record for Arun Chougule [Opens in a new window] ,
Mary Joan Open the ORCID record for Mary Joan [Opens in a new window]  and
HS Kumar Open the ORCID record for HS Kumar [Opens in a new window]
M. Athiyaman*
Affiliation:
Radiological Physics, Sardar Patel Medical College, Bikaner, Rajasthan, India334003
A. Hemalatha
Affiliation:
Radiological Physics, Sardar Patel Medical College, Bikaner, Rajasthan, India334003
Arun Chougule
Affiliation:
Radiological Physics SMS Medical College and Hospital, Jaipur, Rajasthan, India302004
Mary Joan
Affiliation:
Radiological Physics SMS Medical College and Hospital, Jaipur, Rajasthan, India302004
HS Kumar
Affiliation:
Radiological Physics, Sardar Patel Medical College, Bikaner, Rajasthan, India334003
*
Author for correspondence: M. Athiyaman, Radiological Physics, Sardar Patel Medical College, Bikaner, Rajasthan, India334003. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction:

The control points (CP) play a significant role in the delivery of segmented based Intensity-Modulated Radiation Therapy (IMRT) delivery, particularly in dynamic mode. The number of segments is determined by control points and these segments will transfer from one to the other either during beam ON called dynamic delivery or during beam OFF called static delivery or step and shoot. This study was aimed at indirect estimation of the total monitor units (MU) to be delivered per field by exploiting the control points and also to find the MUs at any nth segment.

Materials and methods:

This study was performed in the Eclipse treatment planning software version 13.8.0. The details of control points, metre set weight per segment, leaf positions for each segment, field size, etc. were taken into consideration.

Results:

TPS calculated MU value and analytically estimated MU value were compared and the percentage of difference was estimated. The overall mean percentage of deviation was 1·03% between the TPS calculated method and the analytical method. The paired sample t-test was performed and, p-value <0·05, no significant difference was found. The analytical relationship determined to estimate the total number of MU delivered for any nth control point was also evaluated.

Conclusion:

The control points are a potential parameter in the conventional IMRT delivery. Through this study, we have addressed the indirect method to estimate the monitor units delivered per segment.

Keywords

computer-assisted radiotherapyradiotherapy dosageradiotherapy planning
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 21 , Issue 3 , September 2022 , pp. 343 - 350
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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

Jabbari, K, Amouheidari, A, Babazadeh, S. The Quality Control of Intensity Modulated Radiation Therapy (IMRT) for ONCOR Siemens Linear Accelerators Using Film Dosimetry. y. Iran J Med Phys 2012; 9 (2):111–125.Google Scholar
Ma, C M, Mok, E, Kapur, A et al. Clinical implementation of a Monte Carlo treatment planning system. Med Phys 1999; 26 (10): 2133–2143.CrossRefGoogle ScholarPubMed
Al-Rahbi, Z S, Al Mandhari, Z, Ravichandran, R, et al. Dosimetric comparison of intensity modulated radiotherapy isocentric field plans and field in field (FIF) forward plans in the treatment of breast cancer. J Med Phys 2013; 38 (1): 22–29.Google ScholarPubMed
Khosla, D, Patel, F D, Oinam, A S, Tomar, P, Sharma, S C. Dosimetric comparison of vaginal vault ovoid brachytherapy versus intensity-modulated radiation therapy plans in postoperative patients of cervical carcinoma following whole pelvic radiotherapy. J Cancer Res Ther 2014; 10 (1): 153–158.CrossRefGoogle ScholarPubMed
Khan, M I, Jiang, R, Kiciak, A, Ur Rehman, J, Afzal, M, Chow, J C. Dosimetric and radiobiological characterizations of prostate intensity-modulated radiotherapy and volumetric-modulated arc therapy: a single-institution review of ninety cases. J Med Phys 2016; 41 (3): 162–168.Google ScholarPubMed
Wu, V W C, Leung, K Y. A Review on the Assessment of Radiation Induced Salivary Gland Damage After Radiotherapy. Front Oncol 2019; 9: 1090.CrossRefGoogle ScholarPubMed
Boero, I J, Paravati, A J, Xu, B et al. Importance of Radiation Oncologist Experience Among Patients With Head-and-Neck Cancer Treated With Intensity-Modulated Radiation Therapy. J Clin Oncol 2016; 34 (7): 684–690.CrossRefGoogle ScholarPubMed
Convery, D J, Rosenbloom, L. The generation of intensity-modulated fields for conformal radiotherapy by dynamic collimation. Phys Med Biol 1991; 37: 1359–1374.CrossRefGoogle Scholar
Chui, C S, LoSasso, T, Spirou, S. Dose calculation for photon beams with intensity modulation generated by dynamic jaw or multileaf collimations. Med Phys 1994; 21 (8): 1237–1244.CrossRefGoogle ScholarPubMed
Chang, S X, Cullip, T J, Deschesne, K M. Intensity modulation delivery techniques: “step & amp; shoot” MLC auto-sequence versus the use of a modulator. Med Phys 2000; 27: 948–959.CrossRefGoogle Scholar
Xia, P, Verhey, L J. Multileaf collimator leaf sequencing algorithm for intensity modulated beams with multiple static segments. Med Phys 1998; 25 (8): 1424–1434.CrossRefGoogle ScholarPubMed
Galvin, J M, Chen, X G, Smith, R M. Combining multileaf fields to modulate fluence distributions. Int J Radiat Oncol Biol Phys 1993; 27 (3): 697–705.CrossRefGoogle ScholarPubMed
Chui, C S, Spirou, S, LoSasso, T. Testing of dynamic multileaf collimation. Med Phys 1996; 23 (5): 635–641.CrossRefGoogle ScholarPubMed
Otto, K. Volumetric modulated arc therapy: IMRT in a single gantry arc. Med Phys 2008; 35 (1): 310–317.CrossRefGoogle Scholar
Yu, C X. Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy. Phys Med Biol 1995; 40 (9): 1435–1449.CrossRefGoogle ScholarPubMed
LoSasso, T, Chui, C S, Ling, C C. Physical and dosimetric aspects of a multileaf collimation system used in the dynamic mode for implementing intensity modulated radiotherapy. Med Phys 1998; 25 (10): 1919–1927.CrossRefGoogle ScholarPubMed
Van Esch, A, Huyskens, D P, Behrens, C F et al. Implementing Rapid Arc into clinical routine: a comprehensive program from machine QA to TPS validation and patient QA. Med Phys 2011; 38 (9): 5146–5166.CrossRefGoogle Scholar
Ling, C C, Burman, C, Chui, C S et al. Conformal radiation treatment of prostate cancer using inversely-planned intensity-modulated photon beams produced with dynamic multileaf collimation. Int J Radiat Oncol Biol Phys 1996; 35 (4): 721–730.CrossRefGoogle ScholarPubMed
Mohan, R, Arnfield, M, Tong, S, Wu, Q, Siebers, J. The impact of fluctuations in intensity patterns on the number of monitor units and the quality and accuracy of intensity modulated radiotherapy. Med Phys 2000; 27 (6): 1226–1237.CrossRefGoogle ScholarPubMed
Tsai, J S, Wazer, D E, Ling, M N, Wu, J K et al. Dosimetric verification of the dynamic intensity-modulated radiation therapy of 92 patients. Int J Radiat Oncol Biol Phys 1998; 40 (5): 1213–1230.CrossRefGoogle ScholarPubMed
Chen, Z, Xing, L, Nath, R. Independent monitor unit calculation for intensity modulated radiotherapy using the MIMiC multileaf collimator. Med Phys 2002; 29 (9): 2041–2051.CrossRefGoogle ScholarPubMed
Georg, D, Garibaldi, C, Dutreix, A, Output ratios in a mini phantom for asymmetric fields shaped by a multileaf collimator. Phys Med Biol 1997; 42 (11); 2305–2317.CrossRefGoogle Scholar
Xing, L, Chen, Y, Luxton, G, Li, J G, Boyer, A L. Monitor unit calculation for an intensity modulated photon field by a simple scatter-summation algorithm. Phys Med Biol 2000; 45 (3): N1–N7.CrossRefGoogle ScholarPubMed
Williams, P C. IMRT: delivery techniques and quality assurance. Br J Radiol 2003;76 (911): 766–776.CrossRefGoogle ScholarPubMed
Wang, X, Spirou, S, LoSasso, T, Stein, J, Chui, C S, Mohan, B. Dosimetric verification of intensity-modulated fields. Med Phys 1996; 23 (3): 317–327.CrossRefGoogle ScholarPubMed
Xing, L, Curran, B, Hill, R, Holmes, T, Ma, L, Forster, K M, Boyer, A L. Dosimetric verification of a commercial inverse treatment planning system. Phys Med Biol 1999; 44 (2): 463–478.CrossRefGoogle ScholarPubMed
Ludlum, E, Akazawa, C, Xia P: IMRT plans can be simplified using one step optimization. Med Phys 2006; 33 (6): 2111.CrossRefGoogle Scholar
IMRT, IGRT, SBRT- Advances in the Treatment Planning and delivery of Radiotherapy, 2nd Edition.Google Scholar
Galvin, J M, Ezzell, G, Eisbrauch, A, et al. 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 (5): 1616–1634.CrossRefGoogle Scholar
Hall, E J, Wuu, C S. Radiation-induced second cancers: the impact of 3D-CRT and IMRT. Int J Radiat Oncol Biol Phys 2003; 56 (1): 83–88.CrossRefGoogle ScholarPubMed
Hansen, E K, Bucci, M K, Quivey, J M, Weinberg, V, Xia, P. Repeat CT imaging and replanning during the course of IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2006; 64 (2): 355–362.CrossRefGoogle ScholarPubMed
Yan, D, Vicini, F, Wong, J, Martinez A: adaptive radiation therapy. Phys Med Biol 1997; 42: 123–132.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 (3): 807–821.CrossRefGoogle ScholarPubMed
Kamath, S, Sahni, S, Li, J, Palta, J, Ranka, S. Leaf sequencing algorithms for segmented multileaf collimation. Phys Med Biol 2003; 48 (3): 307–324.CrossRefGoogle ScholarPubMed
Siebers, J V, Lauterbach, M, Keall, P J, Mohan, R. Incorporating multi-leaf collimator leaf sequencing into iterative IMRT optimization. Med Phys 2002; 29 (6): 952–959.CrossRefGoogle ScholarPubMed
Sun, X, Xia, P. A new smoothing procedure to reduce delivery segments for static MLC-based IMRT planning. Med Phys 2004; 31 (5): 1158–1165.CrossRefGoogle ScholarPubMed
Crooks, S M, McAven, L F, Robinson, D F, Xing, L. Minimizing delivery time and monitor units in static IMRT by leaf-sequencing. Phys Med Biol 2002; 47 (17): 3105–3116.CrossRefGoogle ScholarPubMed
Bednarz, G, Michalski, D, Houser, C, Huq, M S, Xiao, Y, Anne, P R, Galvin, J M. The use of mixed-integer programming for inverse treatment planning with pre-defined field segments. Phys Med Biol 2002; 47 (13): 2235–2245.CrossRefGoogle ScholarPubMed
Spirou, S V, Fournier-Bidoz, N, Yang, J, Chui, C S, Ling, C C. Smoothing intensity-modulated beam profiles to improve the efficiency of delivery. Med Phys 2001; 28 (10): 2105–2112.CrossRefGoogle ScholarPubMed
Coselmon, M M, Moran, J M, Radawski, J D, Fraass, B A. Improving IMRT delivery efficiency using intensity limits during inverse planning. Med Phys 2005; 32 (5): 1234–1245.CrossRefGoogle ScholarPubMed
Keller-Reichenbecher, M A, Bortfeld, T, Levegrün, S, Stein, J, Preiser, K, Schlegel, W. Intensity modulation with the “step and shoot” technique using a commercial MLC: a planning study. Multileaf collimator. Int J Radiat Oncol Biol Phys 1999; 45 (5): 1315–1324.CrossRefGoogle ScholarPubMed
Seco, J, Evans, P M, Webb, S. An optimization algorithm that incorporates IMRT delivery constraints Phys Med Biol 2002; 47: 899–915.Google ScholarPubMed
IMRT, IGRT, SBRT- Advances in the Treatment Planning and delivery of Radiotherapy, 2nd Edition.Google Scholar