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. K. Kam et al., “Prospective randomized study of intensity-modulated radiotherapy on salivary gland function in early-stage nasopharyngeal carcinoma patients,” J. Clin. Oncol. 25, 48734879 (2007).
2.C. B. Simone II et al., “Comparison of intensity-modulated radiotherapy, adaptive radiotherapy, proton radiotherapy, and adaptive proton radiotherapy for treatment of locally advanced head and neck cancer,” Radiother. Oncol.: J. Eur. Soc. Ther. Radiol. Oncol. 101, 376382 (2011).
3.T. A. van de Water, A. J. Lomax, H. P. Bijl, M. E. de Jong, C. Schilstra, E. B. Hug, and J. A. Langendijk, “Potential benefits of scanned intensity-modulated proton therapy versus advanced photon therapy with regard to sparing of the salivary glands in oropharyngeal cancer,” Int. J. Radiat. Oncol., Biol., Phys. 79, 12161224 (2011).
4.J. L. Barker et al., “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, 960970 (2004).
5.W. Wang et al., “Clinical study of the necessity of replanning before the 25th fraction during the course of intensity-modulated radiotherapy for patients with nasopharyngeal carcinoma,” Int. J. Radiat. Oncol., Biol., Phys. 77, 617621 (2010).
6.A. C. Kraan et al., “Dose uncertainties in IMPT for oropharyngeal cancer in the presence of anatomical, range, and setup errors,” Int. J. Radiat. Oncol., Biol., Phys. 87, 888896 (2013).
7.A. Trofimov et al., “Interfractional variations in the setup of pelvic bony anatomy and soft tissue, and their implications on the delivery of proton therapy for localized prostate cancer,” Int. J. Radiat. Oncol., Biol., Phys. 80, 928937 (2011).
8.S. Park, M. Cho, and H. Kim, “On-board CBCT/CBDT for image-guided proton therapy: Initial performance evaluation,” Int. J. Radiat. Oncol., Biol., Phys. 75, S595S596 (2009).
9.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.: J. Eur. Soc. Ther. Radiol. Oncol. 93, 17 (2009).
10.J. H. Siewerdsen and D. A. Jaffray, “Cone-beam computed tomography with a flat-panel imager: Magnitude and effects of x-ray scatter,” Med. Phys. 28, 220231 (2001).
11.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, 685705 (2007).
12.E. Bentefour, S. Tang, S. Both, G. Chen, and H. Lu, “TU - A - 204B - 02: On the potential of CBCT for range verification in proton therapy,” Med. Phys. 37, 3370 (2010).
13.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, 522530 (2007).
14.Q. Wu, Y. Chi, P. Y. Chen, D. J. Krauss, D. Yan, and A. Martinez, “Adaptive replanning strategies accounting for shrinkage in head and neck IMRT,” Int. J. Radiat. Oncol., Biol., Phys. 75, 924932 (2009).
15.M. Peroni et al., “Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy,” Int. J. Radiat. Oncol., Biol., Phys. 84, e427e433 (2012).
16.X. Zhen, X. Gu, H. Yan, L. Zhou, X. Jia, and S. B. Jiang, “CT to cone-beam CT deformable registration with simultaneous intensity correction,” Phys. Med. Biol. 57, 68076826 (2012).
17.X. Zhen, H. Yan, L. Zhou, X. Jia, and S. B. Jiang, “Deformable image registration of CT and truncated cone-beam CT for adaptive radiation therapy,” Phys. Med. Biol. 58, 79797993 (2013).
18.C. Veiga et al., “Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CT-to-CBCT deformable registration for ‘dose of the day’ calculations,” Med. Phys. 41, 031703 (12pp.) (2014).
19.G. Landry et al., “Phantom based evaluation of CT to CBCT image registration for proton therapy dose recalculation,” Phys. Med. Biol. 60, 595613 (2015).
20.H. Knutsson and M. Andersson, “Morphons: Paint on priors and elastic canvas for segmentation and registration,” in Image Analysis, edited by H. Kalviainen, J. Parkkinen, and A. Kaarna (Springer, Berlin Heidelberg, Germany, 2005), pp. 292301.
21.A. Wrangsjo, J. Pettersson, and H. Knutsson, “Non-rigid registration using morphons,” in Image Analysis, edited by H. Kalviainen, J. Parkkinen, and A. Kaarna (Springer, Berlin Heidelberg, Germany, 2005), pp. 501510.
22.G. Janssens, L. Jacques, J. O. d. Xivry, X. Geets, and B. Macq, “Diffeomorphic registration of images with variable contrast enhancement,” J. Biomed. Imaging 2011, 112.
23.J. P. Thirion, “Image matching as a diffusion process: An analogy with Maxwell’s demons,” Med. Image Anal. 2, 243260 (1998).
24.G. Janssens, J. O. de Xivry, S. Fekkes, A. Dekker, B. Macq, P. Lambin, and W. van Elmpt, “Evaluation of nonrigid registration models for interfraction dose accumulation in radiotherapy,” Med. Phys. 36, 42684276 (2009).
25.D. G. Lowe, “Object recognition from local scale-invariant features,” in 1999. The Proceedings of the Seventh IEEE International Conference on Computer Vision (IEEE, Corfu, Greece, 1999).
26.D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vision 60, 91110 (2004).
27.W. Cheung and G. Hamarneh, “n-SIFT: n-Dimensional scale invariant feature transform,” Image Process., IEEE Trans. 18, 20122021 (2009).
28.C. Paganelli, M. Peroni, G. Baroni, and M. Riboldi, “Quantification of organ motion based on an adaptive image-based scale invariant feature method,” Med. Phys. 40, 111701 (12pp.) (2013).
29.J. A. Shackleford, N. Kandasamy, and G. C. Sharp, “On developing B-spline registration algorithms for multi-core processors,” Phys. Med. Biol. 55, 63296351 (2010).
30.L. C. G. G. Persoon, M. Podesta, W. J. C. van Elmpt, S. M. J. J. G. Nijsten, and F. Verhaegen, “A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations,” Med. Phys. 38, 40324035 (2011).
31.J. O. Deasy, A. I. Blanco, and V. H. Clark, “CERR: A computational environment for radiotherapy research,” Med. Phys. 30, 979985 (2003).
32.S. Schell and J. J. Wilkens, “Advanced treatment planning methods for efficient radiation therapy with laser accelerated proton and ion beams,” Med. Phys. 37, 53305340 (2010).
33.A. Mencarelli, S. R. van Kranen, O. Hamming-Vrieze, S. van Beek, C. R. Nico Rasch, M. van Herk, and J. J. Sonke, “Deformable image registration for adaptive radiation therapy of head and neck cancer: Accuracy and precision in the presence of tumor changes,” Int. J. Radiat. Oncol., Biol., Phys. 90, 680687 (2014).
34.C. Paganelli et al., “Scale invariant feature transform in adaptive radiation therapy: A tool for deformable image registration assessment and re-planning indication,” Phys. Med. Biol. 58, 287299 (2013).
35.D. Yang, H. H. Li, S. M. Goddu, and J. Tan, “CBCT volumetric coverage extension using a pair of complementary circular scans with complementary kV detector lateral and longitudinal offsets,” Phys. Med. Biol. 59, 63276339 (2014).

Data & Media loading...


Article metrics loading...



Intensity modulated proton therapy (IMPT) of head and neck (H&N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry-mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigated deformable image registration () of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation.

Datasets of six H&N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from to yield a contoured vCT. The accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The generated contours on the vCT were compared to physician-drawn contours on the rpCT.

The accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [−4, 2 Gy] for parotid glands ( ), spinal cord ( ), brainstem ( ), and CTV ( ). When using generated contours for the vCT, those differences ranged within [−8, 11 Gy].

In this work, the authors generated CBCT based stopping power distributions using of the pCT to a CBCT scan. accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of generated contours introduced variability in DVH statistics.


Full text loading...


Access Key

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