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.
Effectiveness of external respiratory surrogates for in vivo liver motion estimation
Rent this article for
Access full text Article
1. J. Ferlay, H.-R. Shin, F. Bray, D. Forman, C. Mathers, and D. M. Parkin, “Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008,” Int. J. Cancer 127 (12), 28932917 (2010).
2. A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global cancer statistics,” Cancer J. Clin. 61(2), 6990 (2011).
3. H. Kaneko, S. Takagi, Y. Otsuka, M. Tsuchiya, A. Tamura, T. Katagiri, T. Maeda, and T. Shiba, “Laparoscopic liver resection of hepatocellular carcinoma,” Am. J. Surg. 189(2), 190194 (2005).
4. G. Belli, P. Limongelli, C. Fantini, A. D’Agostino, L. Cioffi, A. Belli, and G. Russo, “Laparoscopic and open treatment of hepatocellular carcinoma in patients with cirrhosis,” Br. J. Surg. 96(9), 10411048 (2009).
5. A. Laurent, D. Cherqui, M. Lesurtel, F. Brunetti, C. Tayar, and P.-L. Fagniez, “Laparoscopic liver resection for subcapsular hepatocellular carcinoma complicating chronic liver disease,” Arch. Surg. 138(7), 763769 (2003).
6. H. Tranchart, G. Di Giuro, P. Lainas, J. Roudie, H. Agostini, D. Franco, and I. Dagher, “Laparoscopic resection for hepatocellular carcinoma: A matched-pair comparative study,” Surg. Endosc. 24(5), 11701176 (2010).
7. M. J. Murphy, “Tracking moving organs in real time,” Semin. Radiat. Oncol. 14(1), 91100 (2004).
8. T. J. Dubinsky, C. Cuevas, M. K. Dighe, O. Kolokythas, and J. H. Hwang, “High-intensity focused ultrasound: Current potential and oncologic applications,” Am. J. Roentgenol. 190(1), 191199 (2008).
9. Y. F. Zhou, “High intensity focused ultrasound in clinical tumor ablation,” World J. Clin. Oncol. 2(1), 827 (2011).
10. A. Muacevic, B. Wowra, and M. Reiser, “CyberKnife: Review of first 1,000 cases at a dedicated therapy center,” Int. J. Comput. Assist. Radiol. Surg. 3(5), 447456 (2008).
11. T. E. Schefter, B. D. Kavanagh, R. D. Timmerman, H. R. Cardenes, A. Baron, and L. E. Gaspar, “A phase I trial of stereotactic body radiation therapy (SBRT) for liver metastases,” Int. J. Radiat. Oncol., Biol., Phys. 62(5), 13711378 (2005).
12. R. D. Timmerman, B. D. Kavanagh, L. C. Cho, L. Papiez, and L. Xing, “Stereotactic body radiation therapy in multiple organ sites,” J. Clin. Oncol. 25(8), 947952 (2007).
13. Q. J. Wu, D. Thongphiew, W. Zhiheng, V. Chankong, and Y. Fangfang, “The impact of respiratory motion and treatment technique on stereotactic body radiation therapy for liver cancer,” Med. Phys. 35(4), 14401451 (2008).
14. P. Weiss, J. Baker, and E. Potchen, “Assessment of hepatic respiratory excursion,” J. Nucl. Med. 13, 758759 (1972).
15. S. C. Davies, A. L. Hill, R. B. Holmes, M. Halliwell, and P. C. Jackson, “Ultrasound quantitation of respiratory organ motion in the upper abdomen,” Br. J. Radiol. 67(803), 10961102 (1994).
16. K. M. Langen and D. T. L. Jones, “Organ motion and its management,” Int. J. Radiat. Oncol. Biol. Phys. 50(1), 265278 (2001).
17. B. Bussels, L. Goethals, M. Feron, D. Bielen, S. Dymarkowski, P. Suetens, and K. Haustermans, “Respiration-induced movement of the upper abdominal organs: A pitfall for the three-dimensional conformal radiation treatment of pancreatic cancer,” Radiother. Oncol. 68(1), 6974 (2003).
18. A. Kirilova, G. Lockwood, P. Choi, N. Bana, M. A. Haider, K. K. Brock, C. Eccles, and L. A. Dawson, “Three-dimensional motion of liver tumors using cine-magnetic resonance imaging,” Int. J. Radiat. Oncol., Biol., Phys. 71(4), 11891195 (2008).
19. S. S. Vedam, P. J. Keall, V. R. Kini, and R. Mohan, “Determining parameters for respiration-gated radiotherapy,” Med. Phys. 28(10), 21392146 (2001).
20. Y. Seppenwoolde, H. Shirato, K. Kitamura, S. Shimizu, M. van Herk, J. V. Lebesque, and K. Miyasaka, “Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 53(4), 822834 (2002).
21. H. D. Kubo and B. C. Hill, “Respiration gated radiotherapy treatment: A technical study,” Phys. Med. Biol. 41(1), 8391 (1996).
22. S. Hunjan, G. Starkschall, K. Prado, L. Dong, and P. Balter, “Lack of correlation between external fiducial positions and internal tumor positions during breath-hold CT,” Int. J. Radiat. Oncol., Biol., Phys. 76(5), 15861591 (2010).
23. I. C. Laura, A. K. Y. Chao, A. Sandhu, and S. B. Jiang, “The diaphragm as an anatomic surrogate for lung tumor motion,” Phys. Med. Biol. 54(11), 35293541 (2009).
24. 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).
25. T. Zhang, H. Keller, M. J. O’Brien, T. R. Mackie, and B. Paliwal, “Application of the spirometer in respiratory gated radiotherapy,” Med. Phys. 30(12), 31653171 (2003).
26. J. D. P. Hoisak, K. E. Sixel, R. Tirona, P. C. F. Cheung, and J.-P. Pignol, “Correlation of lung tumor motion with external surrogate indicators of respiration,” Int. J. Radiat. Oncol., Biol., Phys. 60(4), 12981306 (2004).
27. Y. Tsunashima, T. Sakae, Y. Shioyama, K. Kagei, T. Terunuma, A. Nohtomi, and Y. Akine, “Correlation between the respiratory waveform measured using a respiratory sensor and 3D tumor motion in gated radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 60(3), 951958 (2004).
28. H. D. Kubo, P. M. Len, S. i. Minohara, and H. Mostafavi, “Breathing-synchronized radiotherapy program at the University of California Davis Cancer Center,” Med. Phys. 27(2), 346353 (2000).
29. K. T. Malinowski, J. R. Pantarotto, S. Senan, T. J. McAvoy, and W. D. D’Souza, “Inferring positions of tumor and nodes in stage III lung cancer from multiple anatomical surrogates using four-dimensional computed tomography,” Int. J. Radiat. Oncol., Biol., Phys. 77(5), 15531560 (2010).
30. H. Yan, F. Yin, G. 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).
31. B. Cho, P. R. Poulsen, A. Sawant, D. Ruan, and P. J. Keall, “Real-time target position estimation using stereoscopic kilovoltage/megavoltage imaging and external respiratory monitoring for dynamic multileaf collimator tracking,” Int. J. Radiat. Oncol., Biol., Phys. 79(1), 269278 (2011).
32. K. Malinowski, T. J. McAvoy, R. George, S. Dietrich, and W. D. D’Souza, “Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions,” Int. J. Radiat. Oncol., Biol., Phys. 82(5), 16651673 (2012).
33. M. Nakamura, Y. Narita, Y. Matsuo, M. Narabayashi, M. Nakata, A. Sawada, T. Mizowaki, Y. Nagata, and M. Hiraoka, “Effect of audio coaching on correlation of abdominal displacement with lung tumor motion,” Int. J. Radiat. Oncol., Biol., Phys. 75(2), 558563 (2009).
34. S. Weisberg, Applied Linear Regression, 3rd ed. (John Wiley & Sons, Inc., Hoboken, NJ, 2005).


Image of FIG. 1.

Click to view

FIG. 1.

The location of trakSTAR sensors on the pig liver (IVC, TRL, and TLL) and the chest/abdomen (C1–A9). The AP-LR-CC coordinates are shown in the lower right corner.

Image of FIG. 2.

Click to view

FIG. 2.

The first signal is the measured abdomen motion on the AP-axis. The second signal is the measured liver motion on the CC, AP, and LR-axes. The third signal is the estimated liver motion on the CC, AP, and LR-axes. The flow chart of the liver-motion estimation approach is shown in this figure below. The first stage of the flow chart is model-fitting; the second stage is liver-motion estimation. Estimation error is the difference between the measured liver motion and the estimated liver motion.

Image of FIG. 3.

Click to view

FIG. 3.

(a) Correlation models (linear models) between the abdomen motion on the AP-axis and the liver movement of IVC on the CC, AP, and LR-axes. (b) The bar charts of the modeling errors.

Image of FIG. 4.

Click to view

FIG. 4.

Liver movement at IVC, TRL, and TLL. Main movement is in the CC-direction, and second main movement is in the AP-direction. Movement in the LR-direction is less than in the CC and AP directions. Movement at IVC is larger than at TRL and TLL.

Image of FIG. 5.

Click to view

FIG. 5.

Trajectories of the external respiratory surrogates at C3 and A2. Surrogate A2 has larger movement than C3 has. Main movement is in the AP-direction, and second main movement is in the CC-direction. Movement in the LR-direction is less than in the CC and AP directions.

Image of FIG. 6.

Click to view

FIG. 6.

Estimation errors of IVC, TRL, and TLL using different external respiratory surrogates for three pigs in this study. Estimation errors of IVC are larger than TRL and TLL. Surrogate A2 has the best estimation accuracy. Surrogate A1, A2, and A3 (the upper abdomen) have better estimation accuracy, and surrogate A7, A8, and A9 (the lower abdomen) have worse estimation accuracy.

Image of FIG. 7.

Click to view

FIG. 7.

Estimation errors of IVC, TRL, and TLL using multisurrogate and single-surrogate for three pigs in this study. Excluding the C2/A2/A8 model at IVC, the correlation models with multisurrogate have better estimation accuracy than single-surrogate models. Estimation errors from the multisurrogate are less than 0.4 mm.

Image of FIG. 8.

Click to view

FIG. 8.

Long-term estimation error of IVC, TRL, and TLL using surrogate A2 for three pigs in this study. Correlation model is built using the first 15 s of signals, estimating liver motion in continuous time. Estimation error is less than1.4 mm in 10 min.

Image of FIG. 9.

Click to view

FIG. 9.

The raw data of liver (IVC) motion and external surrogate signals in 10 min in the case of pig 5. The largest movement of liver is on the CC-axis, and the less movement of liver is on the LR-axis. The motions of the liver and the surrogate are stable with small vibration which is less than 1 mm.


Generic image for table

Click to view


Average amplitude and standard deviation of liver motion in different days for six pigs, and the breathing frequency of each pig.

Generic image for table

Click to view


Average amplitude and standard deviation of chest and abdominal motion for six pigs in the AP-direction, and the breathing frequency of each pig.


Article metrics loading...




Due to low frame rate of MRI and high radiation damage from fluoroscopy and CT, livermotion estimation using external respiratory surrogate signals seems to be a better approach to track liver motion in real-time for livertumor treatments in radiotherapy and thermotherapy. This work proposes a livermotion estimation method based on external respiratory surrogate signals. Animal experiments are also conducted to investigate related issues, such as the sensor arrangement, multisensor fusion, and the effective time period.


Liver motion and abdominal motion are both induced by respiration and are proved to be highly correlated. Contrary to the difficult direct measurement of the liver motion, the abdominal motion can be easily accessed. Based on this idea, our study is split into the model-fitting stage and the motion estimation stage. In the first stage, the correlation between the surrogates and the liver motion is studied and established via linear regression method. In the second stage, the liver motion is estimated by the surrogate signals with the correlation model. Animal experiments on cases of single surrogate signal, multisurrogate signals, and long-term surrogate signals are conducted and discussed to verify the practical use of this approach.


The results show that the best single sensor location is at the middle of the upper abdomen, while multisurrogate models are generally better than the single ones. The estimation error is reduced from 0.6 mm for the single surrogate models to 0.4 mm for the multisurrogate models. The long-term validity of the estimation models is quite satisfactory within the period of 10 min with the estimation error less than 1.4 mm.


External respiratory surrogate signals from the abdomen motion produces good performance for livermotion estimation in real-time. Multisurrogate signals enhance estimation accuracy, and the estimation model can maintain its accuracy for at least 10 min. This approach can be used in practical applications such as the livertumor treatment in radiotherapy and thermotherapy.


Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Effectiveness of external respiratory surrogates for in vivo liver motion estimation