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Simplified material assignment for cone beam computed tomography-based dose calculations of prostate radiotherapy with hip prostheses

Published online by Cambridge University Press:  21 April 2016

Turki Almatani ,
Richard P. Hugtenburg ,
Ryan Lewis ,
Susan Barley  and
Mark Edwards
Turki Almatani*
Affiliation:
College of Medicine, Swansea University, Swansea, Wales, UK
Richard P. Hugtenburg
Affiliation:
College of Medicine, Swansea University, Swansea, Wales, UK Department of Medical Physics and Clinical Engineering, Singleton Hospital, ABM University Health Board, Swansea, Wales, UK
Ryan Lewis
Affiliation:
Department of Medical Physics and Clinical Engineering, Singleton Hospital, ABM University Health Board, Swansea, Wales, UK
Susan Barley
Affiliation:
Oncology Systems Limited, Shrewsbury, UK
Mark Edwards
Affiliation:
Department of Medical Physics and Clinical Engineering, Singleton Hospital, ABM University Health Board, Swansea, Wales, UK
*
Correspondence to: Turki Almatani, College of Medicine, Swansea University, Singleton Park, Swansea SA2 8PP, UK. Tel: 44 7423 414495. E-mail: [email protected]
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Abstract

Objective

Cone beam computed tomography (CBCT) images contain more scatter than a conventional computed tomography (CT) image and therefore provide inaccurate Hounsfield units (HUs). Consequently, CBCT images cannot be used directly for dose calculation. The aim of this study is to enable dose calculations to be performed with the use of CBCT images taken during radiotherapy and potentially avoid the necessity of re-planning.

Methodology

A phantom and prostate cancer patient with a metallic prosthetic hip replacement were imaged using both CT and CBCT. The multilevel threshold algorithm was used to categorise pixel values in the CBCT images into segments of homogeneous HU. The variation in HU with position in the CBCT images was taken into consideration and the benefit of using a larger number of materials than typically used in previous work has been explored. This segmentation method relies upon the operator dividing the CBCT data into a set of volumes where the variation in the relationship between pixel values and HUs is small. A field-in-field treatment plan was generated from the CT of the phantom. An intensity-modulated radiation therapy plan was generated from CT images of the patient. These plans were then copied to the segmented CBCT datasets with identical settings and the doses were recalculated and compared.

Results

In the phantom study, γ evaluation showed that the percentage of points falling in planning target volume, rectum and bladder with γ<1 (3%/3 mm) was 100%. In the patient study, increasing the number of bins to define the material type from seven materials to eight materials required 50% more operator time to improve the accuracy by 0·01% using pencil beam and collapsed cone and 0·05% when using Monte Carlo algorithms.

Conclusion

The segmentation of CBCT images using the method in this study can be used for dose calculation. For a simple phantom, 2 values of HU were needed to improve dose calculation accuracy. In challenging circumstances such as that of a prostate patient with hip prosthesis, 5 values of HU were found to be needed, giving a reasonable balance between dose accuracy and operator time.

Keywords

ARTCBCT-based dose calculationMC dose calculationmultilevel threshold algorithm
Type
Original Articles
Information
Journal of Radiotherapy in Practice , Volume 15 , Issue 2 , June 2016 , pp. 170 - 180
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Copyright
© Cambridge University Press 2016 

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References

1.Langen, K M, Jones, D T L. Organ motion and its management. Int J Radiat Oncol Biol Phys 2001; 50 (1): 265–278.Google Scholar
2.Ciernik, I F, Baumert, B G, Egli, P, Glanzmann, C, Ltolf, U M. On-line correction of beam portals in the treatment of prostate cancer using an endorectal balloon device. Radiother Oncol 2002; 65 (1): 39–45.Google Scholar
3.Jaffray, D A, Siewerdsen, J H, Wong, J W, Martinez, A A. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys 2002; 53 (5): 1337–1349.Google Scholar
4.Stock, M, Pasler, M, Birkfellner, W, Homolka, P, Poetter, R, Georg, D. Image quality and stability of image-guided radiotherapy (IGRT) devices: a comparative study. Radiother Oncol 2009; 93 (1): 1–7.Google Scholar
5.Fotina, I, Hopfgartner, J, Stock, M, Steininger, T, Ltgendorf-Caucig, C, Georg, D. Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques. Radiother Oncol 2012; 104 (2): 249–256.Google Scholar
6.van Zijtveld, M, Dirkx, M, Heijmen, B. Correction of conebeam CT values using a planning CT for derivation of the dose of the day. Radiother Oncol 2007; 85 (2): 195–200.Google Scholar
7.Yang, Y, Schreibmann, E, Li, T, Wang, C, Xing, L. Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation. Phys Med Biol 2007; 52 (3): 685–705.Google Scholar
8.Richter, A, Hu, Q, Steglich, Det al. Investigation of the usability of conebeam CT data sets for dose calculation. Radiat Oncol 2008; 3 (1): 42.Google Scholar
9.Boggula, R, Wertz, H, Lorenz, Fet al. A proposed strategy to implement CBCT images for replanning and dose calculations. Int J Radiat Oncol Biol Phys 2007; 69 (3): S655–S656.Google Scholar
10.Boggula, R, Lorenz, F, Abo-Madyan, Yet al. A new strategy for online adaptive prostate radiotherapy based on cone-beam CT. Z Med Phys 2009; 19 (4): 264–276.Google Scholar
11.Onozato, Y, Kadoya, N, Fujita, Yet al. Evaluation of on-board kV cone beam computed tomography based dose calculation with deformable image registration using Hounsfield unit modifications. Int J Radiat Oncol Biol Phys 2014; 89 (2): 416–423.Google Scholar
12.Seaby, A W, Thomas, D W, Ryde, S J S, Ley, G R, Holmes, D. Design of a multiblock phantom for radiotherapy dosimetry applications. Br J Radiol 2002; 75 (889): 56–58.CrossRefGoogle ScholarPubMed
13.Pineda, A R, Siewerdsen, J H, Tward, D J. (eds). Analysis of image noise in 3D cone-beam CT: spatial and Fourier domain approaches under conditions of varying stationarity. Proc SPIE 2008; 6913: 69131Q–69131Q-10.Google Scholar
14.Kawrakow, I, Rogers, D W O. The EGSnrc code system. NRC Report PIRS-701. Ottawa: NRC, 2000.Google Scholar
15.HPC Wales. Wales, UK. http://www.hpcwales.co.uk/. Accessed on 8 December 2015.Google Scholar
16.Deasy, J O, Blanco, A I, Clark, V H. CERR: a computational environment for radiotherapy research. Med Phys 2003; 30 (5): 979–985.Google Scholar
17.Nelms, B E, Simon, J A. A survey on IMRT QA analysis. J Appl Clin Med Phys 2007; 8 (3): 76–90.Google Scholar
18.ICRU. International Commission on Radiation Units and Measurements. Prescribing I. Recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). ICRU Report 83. J ICRU 2010; 10: 1–106.Google Scholar
19.Knöös, T, Wieslander, E, Cozzi, Let al. Comparison of dose calculation algorithms for treatment planning in external photon beam therapy for clinical situations. Phys Med Biol 2006; 51 (22): 5785.Google Scholar