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
A new method for tracking organ motion on diagnostic ultrasound images
Rent:
Rent this article for
Access full text Article
/content/aapm/journal/medphys/41/9/10.1118/1.4892065
1.
1. K. Ohara, T. Okumura, M. Akisada, T. Inada, T. Mori, H. Yokota, and M. J. Calaguas, “Irradiation synchronized with respiration gate,” Int. J. Radiat. Oncol., Biol., Phys. 17(4), 853857 (1989).
http://dx.doi.org/10.1016/0360-3016(89)90078-3
2.
2. S. Minohara, T. Kanai, M. Endo, K. Noda, and M. Kanazawa, “Respiratory gated irradiation system for heavy-ion radiotherapy,” Int. J. Radiat. Oncolol., Biol., Phys. 47(4), 10971103 (2000).
http://dx.doi.org/10.1016/S0360-3016(00)00524-1
3.
3. S. S. Vedam, P. J. Keall, V. R. Kini, and R. Mohan, “Determining parameters for respiration-gated radiotherapy,” Med. Phys. 28(10), 21392146 (2001).
http://dx.doi.org/10.1118/1.1406524
4.
4. H. D. Kubo, P. M. Len, S. Minohara, and H. Mostafavi, “Breathing- synchronized radiotherapy program at University of California Davis Cancer Center,” Med. Phys. 27(2), 346353 (2000).
http://dx.doi.org/10.1118/1.598837
5.
5. H. Yan, F. F. Yin, G. P. Zhu, M. Ajlouni, and J. H. Kim, “The correlation evaluation of a tumor tracking system using multiple external markers,” Med. Phys. 33(11), 40734084 (2006).
http://dx.doi.org/10.1118/1.2358830
6.
6. C. W. Stevens, R. F. Munden, K. M. Forster, J. F. Kelly, Z. X. Liao, G. Starkschall, S. Tuchker, and R. Komaki, “Respiratory-driven lung tumor motion is independent of tumor size, tumor location, and pulmonary function,” Int. J. Radiat. Oncol., Biol., Phys. 51(1), 6268 (2001).
http://dx.doi.org/10.1016/S0360-3016(01)01621-2
7.
7. C. Ozhasoglu and M. J. Murphy, “Issues in respiratory motion compensation during external-beam radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 52(5), 13891399 (2002).
http://dx.doi.org/10.1016/S0360-3016(01)02789-4
8.
8. S. S. Vedam, V. R. Kini, P. J. Keall, V. Ramakrishnan, H. Mostafavi, and R. Mohan, “Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker,” Med. Phys. 30(4), 505513 (2003).
http://dx.doi.org/10.1118/1.1558675
9.
9. H. Shirato, S. Shimizu, T. Kunieda, K. Kitamura, M. van Herk, K. Kagei, T. Nishioka, S. Hashimoto, K. Fujita, H. Aoyama, K. Tsuchiya, K. Kudo, and K. Miyasaka, “Physical aspects of a real-time tumor-tracking system for gated radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 48(4), 11871195 (2000).
http://dx.doi.org/10.1016/S0360-3016(00)00748-3
10.
10. K. Takayama, T. Mizowaki, M. Kokubo, N. Kawada, H. Nakayama, Y. Narita, K. Nagano, Y. Kamino, and M. Hiraoka, “Initial validations for pursuing irradiation using a gimbals tracking system,” Radiother. Oncol. 93(1), 4549 (2009).
http://dx.doi.org/10.1016/j.radonc.2009.07.011
11.
11. F. Jacso, A. Kouznetsov, and W. L. Smith, “Development and evaluation of an ultrasound-guided tracking and gating system for hepatic radiotherapy,” Med. Phys. 36(12), 56335640 (2009).
http://dx.doi.org/10.1118/1.3250893
12.
12. A. Sawada, K. Yoda, M. Kokubo, T. Kunieda, Y. Nagata, and M. Hiraoka, “A technique for noninvasive respiratory gated radiation treatment system based on a real time 3D ultrasound image correlation: A phantom study,” Med. Phys. 31(2), 245250 (2004).
http://dx.doi.org/10.1118/1.1634482
13.
13. J. Schlosser, K. Salisbury, and D. Hristov, “Telerobotic system concept for real-time soft-tissue imaging during radiotherapy beam delivery,” Med. Phys. 37(12), 63576367 (2010).
http://dx.doi.org/10.1118/1.3515457
14.
14. A. Hsu, N. R. Miller, P. M. Evans, J. C. Bamber, and S. Webb, “Feasibility of using ultrasound for real-time tracking during radiotherapy,” Med. Phys. 32(6), 15001512 (2005).
http://dx.doi.org/10.1118/1.1915934
15.
15. M. A. L. Bell, B. C. Byram, E. J. Harris, P. M. Evans, and J. C. Bamber, “In vivo liver tracking with a high volume rate 4D ultrasound scanner and a 2D matrix array probe,” Phys. Med. Biol. 57(5), 13591374 (2012).
http://dx.doi.org/10.1088/0031-9155/57/5/1359
16.
16. R. L. Maurice, and M. Bertrand, “Lagrangian speckle model and tissue-motion estimation-theory,” IEEE Trans. Med. Imaging 18(7), 593603 (1999).
http://dx.doi.org/10.1109/42.790459
17.
17. C. Pellot-Barakat, F. Frouin, M. F. Insana, and A. Herment, “Ultrasound elastography based on multiscale estimations of regularized displacement fields,” IEEE Trans. Med. Imaging 23(2), 153163 (2004).
http://dx.doi.org/10.1109/TMI.2003.822825
18.
18. J. Jiang and T. J. Hall, “A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging,” Phys. Med. Biol. 52(13), 37733790 (2007).
http://dx.doi.org/10.1088/0031-9155/52/13/008
19.
19. F. Yeung, S. F. Levinson, D. Fu, and K. J. Parker, “Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh,” IEEE Trans. Med. Imaging 17(6), 945956 (1998).
http://dx.doi.org/10.1109/42.746627
20.
20. R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, “Statistics of speckle in ultrasound b-scans,” IEEE Trans. Sonics Ultras. 30(3), 156163 (1983).
http://dx.doi.org/10.1109/T-SU.1983.31404
21.
21. S. Minohara and A. Higashimata, Japan patent JP2010-144023 (2010).
22.
22. B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proc.IJCAI 2, 674679 (1981).
23.
23. C. Tomasi and T. Kanade, “Detection and tracking of point features,” Carnegie Mellon University Technical Report CMU-CS-91-132, 1991.
24.
24. J. Y. Bouguet, “Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm,” OpenCV Documentation, 2004.
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/9/10.1118/1.4892065
Loading
/content/aapm/journal/medphys/41/9/10.1118/1.4892065
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/41/9/10.1118/1.4892065
2014-08-12
2014-12-19

Abstract

Respiratory-gated irradiation is effective in reducing the margins of a target in the case of abdominal organs, such as the liver, that change their position as a result of respiratory motion. However, existing technologies are incapable of directly measuring organ motion in real-time during radiation beam delivery. Hence, the authors proposed a novel quantitative organ motion tracking method involving the use of diagnostic ultrasound images; it is noninvasive and does not entail radiation exposure. In the present study, the authors have prospectively evaluated this proposed method.

The method involved real-time processing of clinical ultrasound imaging data rather than organ monitoring; it comprised a three-dimensional ultrasound device, a respiratory sensing system, and two PCs for data storage and analysis. The study was designed to evaluate the effectiveness of the proposed method by tracking the gallbladder in one subject and a liver vein in another subject. To track a moving target organ, the method involved the control of a region of interest (ROI) that delineated the target. A tracking algorithm was used to control the ROI, and a large number of feature points and an error correction algorithm were used to achieve long-term tracking of the target. Tracking accuracy was assessed in terms of how well the ROI matched the center of the target.

The effectiveness of using a large number of feature points and the error correction algorithm in the proposed method was verified by comparing it with two simple tracking methods. The ROI could capture the center of the target for about 5 min in a cross-sectional image with changing position. Indeed, using the proposed method, it was possible to accurately track a target with a center deviation of 1.54 ± 0.9 mm. The computing time for one frame image using our proposed method was 8 ms. It is expected that it would be possible to track any soft-tissue organ or tumor with large deformations and changing cross-sectional position using this method.

The proposed method achieved real-time processing and continuous tracking of the target organ for about 5 min. It is expected that our method will enable more accurate radiation treatment than is the case using indirect observational methods, such as the respiratory sensor method, because of direct visualization of the tumor. Results show that this tracking system facilitates safe treatment in clinical practice.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/41/9/1.4892065.html;jsessionid=13av7eiltapup.x-aip-live-06?itemId=/content/aapm/journal/medphys/41/9/10.1118/1.4892065&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: A new method for tracking organ motion on diagnostic ultrasound images
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/9/10.1118/1.4892065
10.1118/1.4892065
SEARCH_EXPAND_ITEM