Abstract
We have clinically evaluated the accuracy of an automatic patient-positioning system based on the image correlation of two edge images in radiotherapy. Ninety-six head & neck images from eight patients undergoing proton therapy were compared with a digitally reconstructed radiograph (DRR) of planning CT. Two edge images, a reference image and a test image, were extracted by applying a Canny edge detector algorithm to a DRR and a 2D X-ray image, respectively, of each patient before positioning. In a simulation using a humanoid phantom, performed to verify the effectiveness of the proposed method, no registration errors were observed for given ranges of rotation, pitch, and translation in the x, y, and z directions. For real patients, however, there were discrepancies between the automatic positioning method and manual positioning by physicians or technicians. Using edged head coronal- and sagittal-view images, the average differences in registration between these two methods for the x, y, and z directions were 0.11 cm, 0.09 cm and 0.11 cm, respectively, whereas the maximum discrepancies were 0.34 cm, 0.38 cm, and 0.50 cm, respectively. For rotation and pitch, the average registration errors were 0.95° and 1.00°, respectively, and the maximum errors were 3.6° and 2.3°, respectively. The proposed automatic patient-positioning system based on edge image comparison was relatively accurate for head and neck patients. However, image deformation during treatment may render the automatic method less accurate, since the test image many differ significantly from the reference image.
Similar content being viewed by others
References
Langer M, Leong J: Optimization of beam weights under dose–volume restrictions. Int J Radiat Oncol Biol Phys 13:1255–1260, 1987
Miften MM, Das SK, Su M, Marks LB: A dose–volume-based tool for evaluating and ranking IMRT treatment plans. J Appl Clin Med Phys 5:1–14, 2004
Morill SM, Lane RG, Wong JA, Rosen II: Dose–volume considerations with linear programming optimization. Med Phys 18:1201–1210, 1991
Lips IM, van der Heide UA, Kotte AN, van Vulpen M, Bel A: Effect of translational and rotational errors on complex dose distributions with off-line and on-line position verification. Int J Radiat Oncol Biol Phys 74:1600–1608, 2009
Engelsman M, Kooy HM: Target volume dose considerations in proton beam treatment planning for lung tumors. Med Phys 32:3549–3557, 2005
Uríe M, Goítein M, Wagner M: Compensating for heterogeneities in proton radiation therapy. Phys Med Biol 29:553–566, 1984
Rabinowitz I, Broomberg J, Goitein M, McCarthy K, Leong J: Accuracy of radiation field alignment in clinical practice. Int J Radiat Oncol Biol Phys 11:1857–1867, 1985
Leszczynski KW, Loose S, Boyko S: An image registration scheme applied to verification of radiation therapy. Br J Radiol 71:413–426, 1998
Girouard LM, Pouliot J, Maldague X, Zaccarin A: Automatic setup deviation measurements with electronic portal images for pelvic fields. Med Phys 25:1180–1185, 1998
Eilertsen K, Skretting A, Tennvassås T: Methods for fully automated verification of patient set-up in external beam radiotherapy with polygon shaped fields. Phys Med Biol 39:993–1012, 1994
Kim J, Fessler JA, Lam KL, Balter JM, Ten Haken RK: A feasibility study of mutual information based setup error estimation for radiotherapy. Med Phys 28:2507–2517, 2001
Plattard D, Soret M, Troccaz J, et al: Patient set-up using portal images: 2D/2D image registration using mutual information. Comput Aided Surg 5:246–262, 2000
Radcliffe T, Rajapakshe R, Shalev S: Pseudocorrelation: a fast, robust, absolute, gray-level image alignment algorithm. Med Phys 21:761–769, 1994
Jones SM, Boyer AL: Investigation of an FFT-based correlation technique for verification of radiation treatment setup. Med Phys 18:1116–1125, 1991
Dong L, Boyer AL: An image correlation procedure for digitally reconstructed radiographs and electronic portal images. Int J Radiat Oncol Biol Phys 33:1053–1060, 1996
Petrascu O, Bel A, Linthout N, Verellen D, Soete G, Storme G: Automatic on-line electronic portal image analysis with a wavelet-based edge detector. Med Phys 27:321–329, 2000
Sawada A, Yoda K, Numano M, et al: Patient positioning method based on binary image correlation between two edge images for proton-beam radiation therapy. Med Phys 32:3106–3111, 2005
Stern D, Kurz L: Edge detection in correlated noise using Latin squares models. Pattern Recogn 21:119–129, 1988
Haberstroh J, Kurz L: Line detection in noisy and structured background using Graco-Latin squares, CVGIP. Graph Models Image Proc 55:161–179, 1993
Nahi N, Assefi T: Bayesian recursive image estimation. IEEE Trans Comput 7:734–738, 1972
Prewitt J: Object enhancement and extraction. In: Lipkin BS, Rosenfeld A Eds. Picture Processing and Psychopictorics. New York: Academic, 1970
Kirsh R: Computer determination of the constituent structure of biological images. Comput Biomed Res 4:314–328, 1971
Marr D, Hildreth E: Theory of edge detection. Proc R Soc Lond B Biol Sci 207:187–217, 1980
Haralick R: Digital step edges from zero crossing second directional derivatives. IEEE Trans Pattern Anal Mach Intell PAMI 58–68, 1984
Huechel M: An operator which locates edges in digitized pictures. J Assoc Comput Mach 18:113–125, 1971
Canny J: A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–698, 1986
Acknowledgment
This study was supported by a grant of the Korea Healthcare technology R&D Project, Ministry of Health, Welfare and Family Affairs, Republic of Korea. (A080756).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yoon, M., Cheong, M., Kim, J. et al. Accuracy of an Automatic Patient-Positioning System Based on the Correlation of Two Edge Images in Radiotherapy. J Digit Imaging 24, 322–330 (2011). https://doi.org/10.1007/s10278-009-9269-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-009-9269-6