1887
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.
oa
Validation of deformable image registration algorithms on CT images of ex vivo porcine bladders with fiducial markers
Rent:
Rent this article for
Access full text Article
/content/aapm/journal/medphys/41/7/10.1118/1.4883839
1.
1. M. R. Button and J. N. Staffurth, “Clinical application of image-guided radiotherapy in bladder and prostate cancer,” Clin. Oncol. 22(8), 698706 (2010).
http://dx.doi.org/10.1016/j.clon.2010.06.020
2.
2. H. T. Lotz, F. J. Pos, M. C. C. M. Hulshof, M. van Herk, J. V. Lebesque, J. C. Duppen, and P. Remeijer, “Tumor motion and deformation during external radiotherapy of bladder cancer,” Int. J. Radiat. Oncol., Biol., Phys. 64(5), 15511558 (2006).
http://dx.doi.org/10.1016/j.ijrobp.2005.12.025
3.
3. W. Majewski, I. Wesolowska, H. Urbanczyk, L. Hawrylewicz, B. Schwierczok, and L. Miszczyk, “Dose distribution in bladder and surrounding normal tissues in relation to bladder volume in conformal radiotherapy for bladder cancer,” Int. J. Radiat. Oncol., Biol., Phys. 75(5), 13711378 (2009).
http://dx.doi.org/10.1016/j.ijrobp.2009.01.005
4.
4. L. P. Muren, A. T. Redpath, H. Lord, and D. McLaren, “Image-guided radiotherapy of bladder cancer: Bladder volume variation and its relation to margins,” Radiother. Oncol. 84(3), 307313 (2007).
http://dx.doi.org/10.1016/j.radonc.2007.06.014
5.
5. R. Ahmad, M. S. Hoogeman, S. Quint, J. W. Mens, I. de Pree, and B. J. Heijmen, “Inter-fraction bladder filling variations and time trends for cervical cancer patients assessed with a portable 3-dimensional ultrasound bladder scanner,” Radiother. Oncol. 89(2), 172179 (2008).
http://dx.doi.org/10.1016/j.radonc.2008.07.005
6.
6. T. Rosewall, C. Catton, G. Currie, A. Bayley, P. Chung, J. Wheat, and M. Milosevic, “The relationship between external beam radiotherapy dose and chronic urinary dysfunction–A methodological critique,” Radiother. Oncol. 97(1), 4047 (2010).
http://dx.doi.org/10.1016/j.radonc.2010.08.002
7.
7. F. J. Pos, M. Hulshof, J. Lebesque, H. Lotz, G. van Tienhoven, L. Moonen, and P. Remeijer, “Adaptive radiotherapy for invasive bladder cancer: A feasibility study,” Int. J. Radiat. Oncol., Biol., Phys. 64(3), 862868 (2006).
http://dx.doi.org/10.1016/j.ijrobp.2005.07.976
8.
8. G. J. Meijer, P.-P. van der Toorn, M. Bal, D. Schuring, J. Weterings, and M. de Wildt, “High precision bladder cancer irradiation by integrating a library planning procedure of 6 prospectively generated SIB IMRT plans with image guidance using lipiodol markers,” Radiother. Oncol. 105(2), 174179 (2012).
http://dx.doi.org/10.1016/j.radonc.2012.08.011
9.
9. F. Foroudi, J. Wong, T. Kron, A. Rolfo, A. Haworth, P. Roxby, J. Thomas, A. Herschtal, D. Pham, S. Williams, K. H. Tai, and G. Duchesne, “Online adaptive radiotherapy for muscle-invasive bladder cancer: Results of a pilot study,” Int. J. Radiat. Oncol., Biol., Phys. 81(3), 765771 (2011).
http://dx.doi.org/10.1016/j.ijrobp.2010.06.061
10.
10. T. Kron, D. Pham, P. Roxby, A. Rolfo, and F. Foroudi, “Credentialing of radiotherapy centres for a clinical trial of adaptive radiotherapy for bladder cancer (TROG 10.01),” Radiother. Oncol. 103(3), 293298 (2012).
http://dx.doi.org/10.1016/j.radonc.2012.03.003
11.
11. S. Wognum, L. Bondar, J. Visser, M. C. C. M. Hulshof, M. S. Hoogeman, and A. Bel, “Semi-automated plan-of-the-day selection based on lipiodol markers in adaptive radiotherapy for bladder cancer,” International conference on medical physics, 2013, Brighton, UK. Abstract published in:
11.S. Wognum, L. Bondar, J. Visser, M. C. C. M. Hulshof, M. S. Hoogeman, and A. Bel, Medical Physics International, 1(2), 0273, 2013.
12.
12. E. S. Andersen, L. P. Muren, T. S. Sorensen, K. O. Noe, M. Thor, J. B. Petersen, M. Hoyer, L. Bentzen, and K. Tanderup, “Bladder dose accumulation based on a biomechanical deformable image registration algorithm in volumetric modulated arc therapy for prostate cancer,” Phys. Med. Biol. 57(21), 70897100 (2012).
http://dx.doi.org/10.1088/0031-9155/57/21/7089
13.
13. E. S. Andersen, K. O. Noe, T. S. Sorensen, S. K. Nielsen, L. Fokdal, M. Paludan, J. C. Lindegaard, and K. Tanderup, “Simple DVH parameter addition as compared to deformable registration for bladder dose accumulation in cervix cancer brachytherapy,” Radiother. Oncol. 107(1), 5257 (2013).
http://dx.doi.org/10.1016/j.radonc.2013.01.013
14.
14. D. Sarrut, “Deformable registration for image-guided radiation therapy,” Z. Med. Phys. 16(4), 285297 (2006).
15.
15. D. Yang, S. R. Chaudhari, S. M. Goddu, D. Pratt, D. Khullar, J. O. Deasy, and I. El Naqa, “Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation,” Med. Phys. 36(2), 329338 (2009).
http://dx.doi.org/10.1118/1.3049594
16.
16. H. Wang, L. Dong, M. F. Lii, A. L. Lee, C. R. de, R. Mohan, J. D. Cox, D. A. Kuban, and R. Cheung, “Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 61(3), 725735 (2005).
http://dx.doi.org/10.1016/j.ijrobp.2004.07.677
17.
17. A. Godley, E. Ahunbay, C. Peng, and X. A. Li, “Automated registration of large deformations for adaptive radiation therapy of prostate cancer,” Med. Phys. 36(4), 14331441 (2009).
http://dx.doi.org/10.1118/1.3095777
18.
18. N. Wen, C. Glide-Hurst, T. Nurushev, L. Xing, J. Kim, H. Zhong, D. Liu, M. Liu, J. Burmeister, B. Movsas, and I. J. Chetty, “Evaluation of the deformation and corresponding dosimetric implications in prostate cancer treatment,” Phys. Med. Biol. 57(17), 53615379 (2012).
http://dx.doi.org/10.1088/0031-9155/57/17/5361
19.
19. W. H. Greene, S. Chelikani, K. Purushothaman, J. P. Knisely, Z. Chen, X. Papademetris, L. H. Staib, and J. S. Duncan, “Constrained non-rigid registration for use in image-guided adaptive radiotherapy,” Med. Image Anal. 13(5), 809817 (2009).
http://dx.doi.org/10.1016/j.media.2009.07.004
20.
20. S. Wognum, L. Bondar, A. G. Zolnay, X. Chai, M. C. Hulshof, M. S. Hoogeman, and A. Bel, “Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy,” Med. Phys. 40(2), 021702 (15pp.) (2013).
http://dx.doi.org/10.1118/1.4773040
21.
21. L. Bondar, M. S. Hoogeman, E. M. Vasquez Osorio, and B. J. Heijmen, “A symmetric nonrigid registration method to handle large organ deformations in cervical cancer patients,” Med. Phys. 37(7), 37603772 (2010).
http://dx.doi.org/10.1118/1.3443436
22.
22. E. M. Vasquez Osorio, M. S. Hoogeman, L. Bondar, P. C. Levendag, and B. J. Heijmen, “A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy,” Med. Phys. 36(7), 28482859 (2009).
http://dx.doi.org/10.1118/1.3134242
23.
23. G. E. Christensen, B. Carlson, K. S. Chao, P. Yin, P. W. Grigsby, K. Nguyen, J. F. Dempsey, F. A. Lerma, K. T. Bae, M. W. Vannier, and J. F. Williamson, “Image-based dose planning of intracavitary brachytherapy: Registration of serial-imaging studies using deformable anatomic templates,” Int. J. Radiat. Oncol., Biol., Phys. 51(1), 227243 (2001).
http://dx.doi.org/10.1016/S0360-3016(01)01667-4
24.
24. N. Kirby, C. Chuang, U. Ueda, and J. Pouliot, “The need for application-based adaptation of deformable image registration,” Med. Phys. 40(1), 011702 (10pp.) (2013).
http://dx.doi.org/10.1118/1.4769114
25.
25. K. K. Brock, “Results of a multi-institution deformable registration accuracy study (MIDRAS),” Int. J. Radiat. Oncol., Biol. Phys. 76(2), 583596 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.06.031
26.
26. B. G. Fallone, D. R. Rivest, T. A. Riauka, and A. D. Murtha, “Assessment of a commercially available automatic deformable registration system,” J. Appl. Clin. Med. Phys. 11(3), 31753198 (2010).
27.
27. 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).
http://dx.doi.org/10.1118/1.3013563
28.
28. R. Castillo, E. Castillo, R. Guerra, V. E. Johnson, T. McPhail, A. K. Garg, and T. Guerrero, “A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets,” Phys. Med. Biol. 54(7), 18491870 (2009).
http://dx.doi.org/10.1088/0031-9155/54/7/001
29.
29. U. J. Yeo, J. R. Supple, M. L. Taylor, R. Smith, T. Kron, and R. D. Franich, “Performance of 12 DIR algorithms in low-contrast regions for mass and density conserving deformation,” Med. Phys. 40(10), 101701 (12pp.) (2013).
http://dx.doi.org/10.1118/1.4819945
30.
30. L. Xiong, A. Viswanathan, A. J. Stewart, S. Haker, C. M. Tempany, L. M. Chin, and R. A. Cormack, “Deformable structure registration of bladder through surface mapping,” Med. Phys. 33(6), 18481856 (2006).
http://dx.doi.org/10.1118/1.2198192
31.
31. M. R. Kaus, K. K. Brock, V. Pekar, L. A. Dawson, A. M. Nichol, and D. A. Jaffray, “Assessment of a model-based deformable image registration approach for radiation therapy planning,” Int. J. Radiat. Oncol., Biol., Phys. 68(2), 572580 (2007).
http://dx.doi.org/10.1016/j.ijrobp.2007.01.056
32.
32. R. Varadhan, G. Karangelis, K. Krishnan, and S. Hui, “A framework for deformable image registration validation in radiotherapy clinical applications,” J. Appl. Clin. Med. Phys. 14(1), 40664087 (2013).
http://dx.doi.org/10.1120/jacmp.v14i1.4066
33.
33. W. F. Melick, J. J. Naryka, and J. H. Schmidt, “Experimental studies of ureteral peristaltic patterns in the pig. I. Similarity of pig and human ureter and bladder physiology,” J. Urol. 85, 145148 (1961).
34.
34. D. Yang, S. Brame, N. El, I. A. Aditya, Y. Wu, S. M. Goddu, S. Mutic, J. O. Deasy, and D. A. Low, “Technical note: DIRART–A software suite for deformable image registration and adaptive radiotherapy research,” Med. Phys. 38(1), 6777 (2011).
http://dx.doi.org/10.1118/1.3521468
35.
35. H. Chui and A. Rangarajan, “A new point matching algorithm for non-rigid registration,” Comput. Vis. Image Understand. 89(2–3), 114141 (2003).
http://dx.doi.org/10.1016/S1077-3142(03)00009-2
36.
36. J. P. Thirion, “Image matching as a diffusion process: An analogy with Maxwell's demons,” Med. Image Anal. 2(3), 243260 (1998).
http://dx.doi.org/10.1016/S1361-8415(98)80022-4
37.
37. B. T. T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, and P. Golland, “Spherical demons: Fast surface registration,” Med. Image Comput. Comput. Assist. Interv. 5241, 745753 (2008).
38.
38. H. Wang, L. Dong, J. O’Daniel, R. Mohan, and A. S. Garden, “Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy,” Phys. Med. Biol. 50(12), 28872905 (2005).
http://dx.doi.org/10.1088/0031-9155/50/12/011
39.
39. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” Int. J. Comput. Vis. 12(1), 4377 (1994).
http://dx.doi.org/10.1007/BF01420984
40.
40. B. K. P. Horn and B. G. Schunck, “Determining optical-flow,” Artif. Intell. 17(1–3), 185203 (1981).
http://dx.doi.org/10.1016/0004-3702(81)90024-2
41.
41. A. Bruhn, J. Weickert, and C. Schnorr, “Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods,” Int. J. Comput. Vis. 61(3), 211231 (2005).
http://dx.doi.org/10.1023/B:VISI.0000045324.43199.43
42.
42. J. D. Lawson, E. Schreibmann, A. B. Jani, and T. Fox, “Quantitative evaluation of a cone beam computed tomography (CBCT)-CT deformable image registration method for adaptive radiation therapy,” J. Appl. Clin. Med. Phys. 8(4), 96113 (2007).
http://dx.doi.org/10.1120/jacmp.v8i4.2432
43.
43. F. L. Bookstein, “Principal warps: Thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567585 (1989).
http://dx.doi.org/10.1109/34.24792
44.
44. A. Vestergaard, L. P. Muren, J. Sondergaard, U. V. Elstrom, M. Hoyer, and J. B. Petersen, “Adaptive plan selection vs. re-optimisation in radiotherapy for bladder cancer: A dose accumulation comparison,” Radiother. Oncol. 109(3), 457462 (2013).
http://dx.doi.org/10.1016/j.radonc.2013.08.045
45.
45. U. J. Yeo, M. L. Taylor, J. R. Supple, R. L. Smith, L. Dunn, T. Kron, and R. D. Franich, “Is it sensible to “deform” dose? 3D experimental validation of dose-warping,” Med. Phys. 39(8), 50655072 (2012).
http://dx.doi.org/10.1118/1.4736534
46.
46. T. Juang, S. Das, J. Adamovics, R. Benning, and M. Oldham, “On the need for comprehensive validation of deformable image registration, investigated with a novel 3-dimensional deformable dosimeter,” Int. J. Radiat. Oncol., Biol., Phys. 87(2), 414421 (2013).
http://dx.doi.org/10.1016/j.ijrobp.2013.05.045
47.
47. S. Korossis, F. Bolland, J. Southgate, E. Ingham, and J. Fisher, “Regional biomechanical and histological characterisation of the passive porcine urinary bladder: Implications for augmentation and tissue engineering strategies,” Biomaterials 30(2), 266275 (2009).
http://dx.doi.org/10.1016/j.biomaterials.2008.09.034
48.
48.See supplementary material at http://dx.doi.org/10.1118/1.4883839 for Figs. S1 and S2. [Supplementary Material]
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/7/10.1118/1.4883839
Loading
/content/aapm/journal/medphys/41/7/10.1118/1.4883839
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/41/7/10.1118/1.4883839
2014-06-30
2014-12-20

Abstract

The spatial accuracy of deformable image registration (DIR) is important in the implementation of image guided adaptive radiotherapy techniques for cancer in the pelvic region. Validation of algorithms is best performed on phantoms with fiducial markers undergoing controlled large deformations. Excised porcine bladders, exhibiting similar filling and voiding behavior as human bladders, provide such an environment. The aim of this study was to determine the spatial accuracy of different DIR algorithms on CT images of porcine bladders with radiopaque fiducial markers applied to the outer surface, for a range of bladder volumes, using various accuracy metrics.

Five excised porcine bladders with a grid of 30–40 radiopaque fiducial markers attached to the outer wall were suspended inside a water-filled phantom. The bladder was filled with a controlled amount of water with added contrast medium for a range of filling volumes (100–400 ml in steps of 50 ml) using a luer lock syringe, and CT scans were acquired at each filling volume. DIR was performed for each data set, with the 100 ml bladder as the reference image. Six intensity-based algorithms (optical flow or demons-based) implemented in the platform DIRART, a b-spline algorithm implemented in the commercial software package VelocityAI, and a structure-based algorithm (Symmetric Thin Plate Spline Robust Point Matching) were validated, using adequate parameter settings according to values previously published. The resulting deformation vector field from each registration was applied to the contoured bladder structures and to the marker coordinates for spatial error calculation. The quality of the algorithms was assessed by comparing the different error metrics across the different algorithms, and by comparing the effect of deformation magnitude (bladder volume difference) per algorithm, using the Independent Samples Kruskal-Wallis test.

The authors found good structure accuracy without dependency on bladder volume difference for all but one algorithm, and with the best result for the structure-based algorithm. Spatial accuracy as assessed from marker errors was disappointing for all algorithms, especially for large volume differences, implying that the deformations described by the registration did not represent anatomically correct deformations. The structure-based algorithm performed the best in terms of marker error for the large volume difference (100–400 ml). In general, for the small volume difference (100–150 ml) the algorithms performed relatively similarly. The structure-based algorithm exhibited the best balance in performance between small and large volume differences, and among the intensity-based algorithms, the algorithm implemented in VelocityAI exhibited the best balance.

Validation of multiple DIR algorithms on a novel physiological bladder phantom revealed that the structure accuracy was good for most algorithms, but that the spatial accuracy as assessed from markers was low for all algorithms, especially for large deformations. Hence, many of the available algorithms exhibit sufficient accuracy for contour propagation purposes, but possibly not for accurate dose accumulation.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/41/7/1.4883839.html;jsessionid=9lda6mnjjfb83.x-aip-live-03?itemId=/content/aapm/journal/medphys/41/7/10.1118/1.4883839&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Validation of deformable image registration algorithms on CT images of ex vivo porcine bladders with fiducial markers
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/7/10.1118/1.4883839
10.1118/1.4883839
SEARCH_EXPAND_ITEM