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. M. Stock, M. Pasler, W. Birkfellner, P. Homolka, R. Poetter, and D. Georg, “Image quality and stability of image-guided radiotherapy (IGRT) devices: A comparative study,” Radiother. Oncol. 93(1), 17 (2009).
2. I. Fotina, J. Hopfgartner, M. Stock, T. Steininger, C. Lütgendorf-Caucig, and D. Georg, “Feasibility of CBCT-based dose calculation: Comparative analysis of HU adjustment techniques,” Radiother. Oncol. 104(2), 249256 (2012).
3. J. R. McClelland, S. Hughes, M. Modat, A. Qureshi, S. Ahmad, D. B. Landau, S. Ourselin, and D. J. Hawkes, “Inter-fraction variations in respiratory motion models,” Phys. Med. Biol. 56(1), 251272 (2011).
4. E. K. Hansen, M. K. Bucci, J. M. Quivey, V. Weinberg, P. Xia, “Repeat CT imaging and replanning during the course of IMRT for head-and-neck cancer,” Int. J. Radiat. Oncol., Biol., Phys. 64(2), 355362 (2006).
5. D. Yan, F. Vicini, J. Wong, and A. Martinez, “Adaptive radiation therapy,” Phys. Med. Biol. 42(1), 123132 (1997).
6. Q. J. Wu, T. Li, Q. Wu, and F. F. Yin, “Adaptive radiation therapy: Technical components and clinical applications,” Cancer J. 17(3), 182189 (2011).
7. L. Xing, J. Siebers, and P. Keall, “Computational challenges for image-guided radiation therapy: Framework and current research,” Semin. Radiat. Oncol. 17, 245257 (2007).
8. S. Y. Tsuji, A. Hwang, V. Weinberg, S. S. Yom, J. M. Quivey, and P. Xia, “Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer,” Int. J. Radiat. Oncol., Biol., Phys. 77(3), 707714 (2010).
9. P. Castadot, J. A. Lee, X. Geets, and V. Grégoire, “Adaptive radiotherapy of head and neck cancer,” Semin. Radiat. Oncol. 20, 8493 (2010).
10. D. L. Schwartz and L. Dong, “Adaptive radiation therapy for head and neck cancer – Can an old goal evolve into a new standard,” J. Oncol. (2011).
11. C. Lee, K. M. Langen, W. Lu, J. Haimerl, E. Schnarr, K. J. Ruchala, G. H. Olivera, S. L. Meeks, P. A. Kupelian, T. D. Shellenberger, and R. R. Mañon, “Evaluation of geometric changes of parotid glands during head and neck cancer radiotherapy using daily MVCT and automatic deformable registration,” Radiother. Oncol. 89(1), 8188 (2008).
12. J. L. Barker, A. S. Garden, K. K. Ang, J. C. O’Daniel, H. Wang, L. E. Court, W. H. Morrison, D. I. Rosenthal, K. S. Chao, S. L. Tucker, R. Mohan, and L. Dong, “Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system,” Int. J. Radiat. Oncol., Biol., Phys. 59(4), 960970 (2004).
13. S. Marzi, P. Pinnarò, D. D’Alessio, L. Strigari, V. Bruzzaniti, C. Giordano, G. Giovinazzo, and L. Marucci, “Anatomical and dose changes of gross tumour volume and parotid glands for head and neck cancer patients during intensity-modulated radiotherapy: Effect on the probability of xerostomia incidence,” Clin. Oncol. (R. Coll. Radiol.) 24(3), e54e62 (2012).
14. D. L. Schwartz, A. S. Garden, S. J. Shah, G. Cheonowski, S. Seipal, D. I. Rosenthal, Y. Chen, Y. Zhang, L. Zhang, P. F. Wong, J. A. Garcia, K. Kian Ang, and L. Dong, “Adaptive radiotherapy for head and neck cancer – Dosimetric results from a prospective clinical trial,” Radiother. Oncol. 106(1), 8084 (2013).
15. D. L. Schwartz, A. S. Garden, J. Thomas, Y. Chen, Y. Zhang, J. Lewin, M. S. Chambers, and L. Dong, “Adaptive radiotherapy for head-and-neck cancer: Initial clinical outcomes from a prospective trial,” Int. J. Radiat. Oncol., Biol., Phys. 83(3), 986993 (2012).
16. A. Richter, Q. Hu, D. Steglich, K. Baier, J. Wilbert, M. Guckenberger, and M. Flentje, “Investigation of the usability of conebeam CT data sets for dose calculation,” Radiat. Oncol. 16, 342 (2008).
17. K. Usui, Y. Ichimaru, Y. Okumura, K. Murakami, M. Seo, E. Kunieda, and K. Ogawa, “Dose calculation with a cone beam CT image in image-guided radiation therapy,” Radiol. Phys. Technol. 6(1), 107114 (2013).
18. H. Guan and H. Dong, “Dose calculation accuracy using cone-beam CT (CBCT) for pelvic adaptive radiotherapy,” Phys. Med. Biol. 54(20), 62396250 (2009).
19. Y. Yang, E. Schreibmann, T. Li, C. Wang, and L. Xing, “Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation,” Phys. Med. Biol. 52(3), 685705 (2007).
20. H. Zhong, J. Kim, and I. J. Chetty, “Analysis of deformable image registration accuracy using computational modelling,” Med. Phys. 37(3), 970979 (2010).
21. T. Zhang, Y. Chi, E. Meldolesi, and D. Yan, “Automatic delineation of on-line head-and-neck computed tomography images: Toward on-line adaptive radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 68(2), 522530 (2007).
22. J. D. Lawson, E. Schreibmann, A. B. Jani, and T. Fox, “Quantitative evaluation of a cone-beam computed tomography-planning computed tomography deformable image registration method for adaptive radiation therapy,” J. Appl. Clin. Med. Phys. 8(4), 96113 (2007).
23. J. Hou, M. Guerrero, W. Chen, and W. D. D’Souza, “Deformable planning CT to cone-beam CT image registration in head-and-neck cancer,” Med. Phys. 38(4), 20882094 (2011).
24. M. Peroni, D. Ciardo, M. F. Spadea, M. Riboldi, S. Comi, G. Baroni, and R. Orecchia, “Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy,” Int. J. Radiat. Oncol., Biol., Phys. 84(3), e427e433 (2012).
25. T. Lo, Y. Yang, E. Schreibmann, T. Li, and L. Xing, “Mapping electron density distribution from Planning CT to cone-beam CT (CBCT): A novel strategy for accurate dose calculation based on CBCT,” Int. J. Radiat. Oncol., Biol., Phys. 63(1), S507 (2005).
26. S. Ourselin, A. Roche, G. Subsol, X. Pennec, and N. Ayache, “Reconstructing a 3D structure from serial histological sections,” Image Vis. Comput. 19, 2531 (2001).
27. D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: Application to breast MR images,” IEEE Trans. Med. Imaging 18(8), 712721 (1999).
28. M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, and S. Ourselin, “Fast free-form deformation using graphics processing unit,” Comput. Methods Programs Biomed. 98(3), 278284 (2010).
29. C. Veiga, J. McClelland, K. Ricketts, D. D’Souza, and G. Royle, “Deformable registrations for head and neck cancer adaptive radiotherapy,” Image-Guidance and Multimodal Dose Planning in Radiation Therapy Workshop of the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France (2012).
30. C. Veiga, J. McClelland, S. Moinuddin, K. Ricketts, M. Modat, S. Ourselin, D. D’Souza, and G. Royle, “Towards adaptive radiotherapy for head and neck patients: Validation of an in-house deformable registration algorithm,” J. Phys.: Conf. Ser. (in press).
31. M. Modat, M. J. Cardoso, P. Daga, D. Cash, N. C. Fox, and S. Ourselin, “Inverse-consistent symmetric free form deformation,” Biomedical Image Registration, edited by B. M. Dawant, G. E. Christensen, J. M. Fitzpatrick, and D. Rueckert, Lecture Notes in Computer Science Vol. 7359 (Springer Berlin Heidelberg, 2012), 7988.
32. M. Modat, J. R. McClelland, and S. Ourselin, “Lung registration using the NiftyReg package,” in Medical Image Analysis for the Clinic: A Grand Challenge Workshop proceedings from the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), edited by B. van Ginneken, K. Murphy, T. Heimann, V. Pekar, and X. Den (Beijing, China, 2010), pp. 3342.
33. G. X. Ding, D. M. Duggan, C. W. Coffey, M. Deeley, D. E. Hallahn, A. Cmelak, and A. Malcolm, “A study on adaptive IMRT treatment planning using kV cone-beam CT,” Radiother. Oncol. 85, 116126 (2007).
34. V. Grégoire, P. Levendag, K. K. Ang, J. Bernier, M. Braaksma, V. Budach, C. Chao, E. Coche, J. S. Cooper, G. Cosnard, A. Eisbruch, S. El-Sayed, B. Emami, C. Grau, M. Hamoir, N. Lee, P. Maingon, K. Muller, and H. Reychler, “CT-based delineation of lymph node levels and related CTVs in the node-negative neck: DAHANCA, EORTC, GORTEC, NCIC, RTOG consensus guidelines,” Radiother. Oncol. 69, 227236 (2003).
35. D. A. Low, W. B. Harms, S. Mutic, and J. A. Purdy, “A technique for the quantitative evaluation of dose distributions,” Med. Phys. 25(5), 656661 (1998).
36. P. Castadot, J. A. Lee, A. Parraga, X. Geets, B. Macq, and V. Grégoire, “Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors,” Radiother. Oncol. 89, 112 (2008).
37. D. A. Jaffray and J. H. Siewerdsen, “Cone-beam computed tomography with a flat-panel imager: Initial performance and characterization,” Med. Phys. 27(6), 13111323 (2000).
38. W. Lu, G. H. Olivera, Q. Chen, K. J. Ruchala, J. Haimer, S. L. Meeks, K. M. Langen, and P. A. Kupelian, “Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy,” Phys. Med. Biol. 51, 43574374 (2006).
39. H. Kato, M. Kanematsu, O. Tanaka, K. Mizuta, M. Aoki, T. Shibata, T. Yamashita, Y. Hirose, and H. Hoshi, “Head and neck squamous cell carcinoma: Usefulness of diffusion-weighted MR imaging in the prediction of a neoadjuvant therapeutic effect,” Eur. Radiol. 19(1), 103109 (2009).
40. H. C. Thoeny, F. De Keyzer, and A. D. King, “Diffusion-weighted MR imaging in the head and neckRadiology 263(1), 1932 (2012).
41. R. Kashani, M. Hub, J. M. Balter, M. L. Kessler, L. Dong, L. Zhang, L. Xing, Y. Xie, D. Hawkes, J. A. Schnabel, J. McClelland, S. Joshi, Q. Chen, and W. Lu, “Objective assessment of deformable image registration in radiotherapy: A multi-institution study,” Med. Phys. 35(12), 59445953 (2008).
42. J. Kim, M. Matuszak, J. Balter, and K. Saitou, “An improved rigidity penalty for deformable registration of head and neck images in intensity-modulated radiation therapy,” Proceedings of IEEE International Conference on Automation Science and Engineering (Seoul, Korea, 2012), pp. 377382.
43. W. Birkfellner, M. Stock, M. Figl, C. Gendrin, J. Hummel, S. Dong, J. Kettenbach, D. Georg, and H. Bergmann, “Stochastic rank correlation: A robust merit function for 2D/3D registration of image data obtained at different energies,” Med. Phys. 36(8), 34203428 (2009).
44. P. Cachier, E. Bardinet, D. Dormont, X. Pennec, and N. Ayache, “Iconic feature based nonrigid registration: The PASHA algorithm,” Comput. Vis. Image Understanding 89, 272298 (2003).

Data & Media loading...


Article metrics loading...



The aim of this study was to evaluate the appropriateness of using computed tomography (CT) to cone-beam CT (CBCT) deformable image registration (DIR) for the application of calculating the “dose of the day” received by a head and neck patient.

NiftyReg is an open-source registration package implemented in our institution. The affine registration uses a Block Matching-based approach, while the deformable registration is a GPU implementation of the popular B-spline Free Form Deformation algorithm. Two independent tests were performed to assess the suitability of our registrations methodology for “dose of the day” calculations in a deformed CT. A geometric evaluation was performed to assess the ability of the DIR method to map identical structures between the CT and CBCT datasets. Features delineated in the planning CT were warped and compared with features manually drawn on the CBCT. The authors computed the dice similarity coefficient (DSC), distance transformation, and centre of mass distance between features. A dosimetric evaluation was performed to evaluate the clinical significance of the registrations errors in the application proposed and to identify the limitations of the approximations used. Dose calculations for the same intensity-modulated radiation therapy plan on the deformed CT and replan CT were compared. Dose distributions were compared in terms of dose differences (DD), gamma analysis, target coverage, and dose volume histograms (DVHs). Doses calculated in a rigidly aligned CT and directly in an extended CBCT were also evaluated.

A mean value of 0.850 in DSC was achieved in overlap between manually delineated and warped features, with the distance between surfaces being less than 2 mm on over 90% of the pixels. Deformable registration was clearly superior to rigid registration in mapping identical structures between the two datasets. The dose recalculated in the deformed CT is a good match to the dose calculated on a replan CT. The DD is smaller than 2% of the prescribed dose on 90% of the body's voxels and it passes a 2% and 2 mm gamma-test on over 95% of the voxels. Target coverage similarity was assessed in terms of the 95%-isodose volumes. A mean value of 0.962 was obtained for the DSC, while the distance between surfaces is less than 2 mm in 95.4% of the pixels. The method proposed provided adequate dose estimation, closer to the gold standard than the other two approaches. Differences in DVH curves were mainly due to differences in the OARs definition (manual vs warped) and not due to differences in dose estimation (dose calculated in replan CT vs dose calculated in deformed CT).

Deforming a planning CT to match a daily CBCT provides the tools needed for the calculation of the “dose of the day” without the need to acquire a new CT. The initial clinical application of our method will be weekly offline calculations of the “dose of the day,” and use this information to inform adaptive radiotherapy (ART). The work here presented is a first step into a full implementation of a “dose-driven” online ART.


Full text loading...


Access Key

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