Abstract
Objective
The large respiratory-induced motion of the liver tumors can affect treatment planning and delivery in many ways. As a result, motion management techniques are necessary to mitigate these effects. An effective approach to reducing the effects of respiratory motion of liver tumors is real-time tracking of the tumor. The Cyberknife treatment modality uses a combination of kV X-ray images, LED markers, an optic camera, and surgically implanted fiducial markers to track liver tumors. However, the use of an invasive method for implanting fiducial markers can lead to complications. We propose a tracking method that requires no fiducial markers for liver tumors by using the projected location of the diaphragm to identify the 3D location of the liver tumor. With the use of the 4D extended cardiac-torso (XCAT) phantom, this simulation study aims to investigate the feasibility of localizing liver tumors through the tracking of the diaphragm-lung border.
Methods
An abdominal 4DCT dataset containing 20 phases of one breathing cycle was created by using the male model of the 4D XCAT phantom. One set of orthogonal DRR images (+ 45°) was generated for each phase. On each DRR image, an outline of the lung-diaphragm border was detected using an edge detection algorithm. The simulated tumor’s gravity center was identified for each phase of the breathing cycle. Using artificial neural networks (ANNs), two respiratory scenarios correlating the diaphragm’s location with the corresponding 3D location of the tumor were compared: (1) lung-defined tumor motion (TL) and (2) user-defined tumor motion (TA). Additionally, using the user-defined tumor motion, we also examined the accuracy of using ANN to track the tumor under the mismatched conditions during 4DCT reconstruction.
Results
Evaluation of the ANN model was quantified by the root mean square error (RMSE) values through the leave-one-out (LOO) validation technique. The RMSE for the TL motion was 0.67 mm and for the TA motion was 0.32 mm. When the ANN model was applied to the mismatched data, it generated the RMSE of 1.63 mm, whereas applied to the ground-truth data, the RMSE is 0.88 mm.
Conclusion
This simulation study shows that the diaphragm and tumor position are closely related. The developed diaphragm disparity-analysis approach, featuring tracking capability and verified with clinically acceptable errors, has the potential to replace fiducial markers for clinical application. The tracking method will be further investigated in clinical datasets from patients.
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References
Shimizu S, Shirato H, Xo B, Kagei K, Nishioka T, Hashimoto S, Tsuchiya K, Aoyama H, Miyasaka K (1999) Three-dimensional movement of a liver tumor detected by high-speed magnetic resonance imaging. Radiother Oncol 50:367–370
Hallman J, Mori S, Sharp G (2012) A four-dimensional computed tomography analysis of multiorgan abdominal motion. Int J Radiat Oncol Biol Phys 83:435–441
Abbas H, Chang B, Chen Z (2014) Motion management in gastrointestinal cancers. J Gastrointest Oncol 5:223–235
Keall P, Mageras G, Balter J et al (2006) The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 33(10):3874–3900
Rosu M, Dawson LA, Balter JM, McShan DL, Lawrence TS, Ten Haken RK (2003) Alterations in normal liver doses due to organ motion. Int J Radiat Oncol Biol Phys 57(5):1472–1479
RadiologyInfo. Fiducial marker placement, January 2016. [Online]. Available: https://www.radiologyinfo.org/en/info.cfm?pg=fiducial-marker
Kim J, Hong S, Kwon S (2012) Safety and efficacy of ultrasound-guided fiducial marker implantation for CyberKnife radiation therapy. Korean J Radiol 13(3):307–313
Roberge D, Cabrera T (2011) Percutaneous liver fiducial implants: techniques, materials and complications. In: Liver biopsy in modern medicine. InTechOpen, Canada
Yang J, Cai J, Wang H, Chang Z, Czito B, Bashir M, Palta M, Yin F (2014) Is diaphragm motion a good surrogate for liver tumor motion? Int J Oncol Biol Phys 90(4):952–958
Balter J, Dawson L, Kazanjian S, McGinn C, Brock K, Lawrence T, Haken R (2001) Determination of ventilatory liver movement via radiographic evaluation of diaphragm position. Int J Radiat Oncol Biol Phys 51(1):267–270
Wang M, Sharp G, Rit S, Delmon V, Wang G (2014) 2D/4D marker-free tumor tracking using 4D CBCT as the reference image. Phys Med Biol 59(9):2219–2233
Goodband JH, Haas OL, Mills JA (2008) A comparison of neural network approaches for on-line prediction in IGRT. Med Phys 35(3):1113–1122
Murphy MJ, Pokhrel D (2009) Optimization of an adaptive neural network to predict breathing. Med Phys 36(1):40–47
Laurent R, Henriet J, Salomon M, Sauget M, Gschwind R, Makovicka L (2012) Respiratory lung motion using an artificial neural network. Neural Comput Applic 21(5):929–934
Yun J, Mackenzie M, Rathee S, Robinson D, Fallone BG (2012) An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking. Med Phys 39(7):4423–4433
Stam M, Crijns S, Zonnenberg B, Barendrecht M, van Vulpen M, Lagendijkl J, Raaymakers B (2012) Navigators for motion detection during real-time MRI-guided radiotherapy. Phys Med Biol 57:6797–6805
Torshabi A, Pella A, Riboldi M, Baroni G (2010) Targeting accuracy in real-time tumor tracking via external surrogates: a comparative study. Technol Cancer Res Treat 9(6):551–561
Segars W, Sturgeon G, Mendonca S, Grimes J, Tsui BMW (2010) 4D XCAT phantom for multimodality imaging research. Med Phys 37(9):4902–4915
Plathow C, Fink C, Ley S (2004) Measurement of diaphragmatic length during the breathing cycle by dynamic MRI: comparison between healthy adults and patients with an intrathoracic tumor. Eur Radiol 14(8):1392–1399
Eken C, Bilge U, Kartal M, Eray O (2009) Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings. Int J Emerg Med 2(2):99–105
McCulloch W, Pitts W (1943) A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133
Rosenberg A (2009) Lesson 5: linear regression with regularization. [Online]. Available: https://www.coursehero.com/file/18384131/lecture5/
Lourakis M (2005) A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found Res Technol 4:1–6
Dan Forsee F, Hagan M (1997) Gauss-Newton approximation to Bayesian learning. [Online]. Available: http://hagan.ecen.ceat.okstate.edu/icnn97a.pdf
Burden F, Winkler D (2008) Bayesian regularization of neural networks. Methods Mol Biol 458:25–44
Lopez I, Aragones L, Villacampa Y, Serra J (2017) Neural network for determining the characteristic points of the bars. Ocean Eng 136:141–151
Low D, White B, Lee P, Thomas D, Gaudio S, Jani S, Wu X, Lamb J (2013) A novel CT acquisition and analysis technique for breathing motion modeling. Phys Med Biol 58(11):L31–L36
Rietzel E, Chen GTY (2006) Improving retrospective sorting of 4D computed tomography data. Med Phys 33:377–379
Acknowledgements
Authors are grateful to Dr. Paul W. Segars and his research group for giving permission to use the 4D XCAT phantom for this research.
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Deon Dick and Weizhao Zhao declare that they have no conflict of interest. Xiaodong Wu and Georges Hatoum declare that they have a US patent for the design of the concept.
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This report does not contain any studies with human participants or animals performed by any of the authors.
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Dick, D., Wu, X., Hatoum, G.F. et al. A fiducial-less tracking method for radiation therapy of liver tumors by diaphragm disparity analysis part 1: simulation study using machine learning through artificial neural network. J Radiat Oncol 7, 275–284 (2018). https://doi.org/10.1007/s13566-018-0358-3
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DOI: https://doi.org/10.1007/s13566-018-0358-3