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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques

verfasst von : Tong Lin, Yucheng Lin

Erschienen in: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Verlag: Springer International Publishing

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Abstract

The respiratory lung tumor motion poses great challenge for radiation therapy of lung cancer patients. Traditional methods leverage external surrogates or implanted markers to indicate the position of tumors, but these methods suffer from inaccuracies or the risk of pneumothorax. In this chapter fluoroscopic images are employed to indicate the tumor position. We show how machine learning techniques can be used for tumor gating and tracking. Experimental results demonstrate the effectiveness of this new method without external or implanted markers. We also discuss some problems about this new method and point out new promising research frontiers.

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Vorheriges Kapitel Radiomics in Medical Imaging—Detection, Extraction and Segmentation
Nächstes Kapitel Image Guided and Robot Assisted Precision Surgery
Literatur
1.
Adler, J.R., Murphy, M.J., Chang, S.D., Hancock, S.L.: Image-guided robotic radiosurgery. Neurosurgery 44, 1299–1307 (1999)
2.
Arslan, S., Yilmaz, A., Bayramgrler, B., Uzman, O., et al.: CT-guided transthoracic fine needle aspiration of pulmonary lesions: accuracy and complications in 294 patients. Int. Med. J. Exp. Clin. Res. textbf8, CR493-497 (2002)
3.
Balter, J.M., Wright, J.N., Newell, L.J., Friemel, B., et al.: Accuracy of a wireless localization system for radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 61, 933–937 (2005)CrossRef
4.
Berbeco, R.I., Mostafavi, H., Sharp, G.C., Jiang, S.B.: Towards fluoroscopic respiratory gating for lung tumours without radiopaque markers. Phys. Med. Biol. 50, 4481–4490 (2005)CrossRef
5.
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011)CrossRef
6.
Cui, Y., Dy, J.G., Sharp, G.C., Alexander, B., Jiang, S.B.: Robust fluoroscopic respiratory gating for lung cancer radiotherapy without implanted fiducial markers. Phys. Med. Biol. 52, 741–755 (2007)CrossRef
7.
Cui, Y., Dy, J.G., Alexander, B., Jiang, S.B.: Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines. Phys. Med. Biol. 53, N315–327 (2008)CrossRef
8.
Cui, Y., Dy, J.G., Sharp, G.C., Alexander, B., Jiang, S.B.: Multiple template-based fluoroscopic tracking of lung tumor mass without implanted fiducial markers. Phys. Med. Biol. 52, 6229–6242 (2007)CrossRef
9.
Geraghty, P.R., Kee, S.T., McFarlane, G., Razavi, M.K., et al.: CT-guided transthoracic needle aspiration biopsy of pulmonary nodules: Needle size and pneumothorax rate. Radiology 229, 475–481 (2003)CrossRef
10.
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall International, Englewood Cliffs (1994)MATH
11.
Jiang, S.B.: Radiotherapy of mobile tumors. Semin. Radiat. Oncol. 16, 239–248 (2006)CrossRef
12.
Jiang, S.B.: Technical aspects of image-guided respiration-gated radiation therapy. Med. Dosimetry 31, 141–151 (2006)CrossRef
13.
Keall, P.J., Joshi, S., Vedam, S.S., Siebers, J.V., et al.: Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. Med. Phys. 32, 942–951 (2005)CrossRef
14.
Keall, P.J., Kini, V.R., Vedam, S.S., Mohan, R.: Motion adaptive x-ray therapy: a feasibility study. Phys. Med. Biol. 46, 1–10 (2001)CrossRef
15.
Lewis, J.H., Li, R., Watkins, W.T., Lawson, J.D., et al.: Markerless lung tumor tracking and trajectory reconstruction using rotational cone-beam projections: a feasibility study. Phys. Med. Biol. 55, 2505–2522 (2010)CrossRef
16.
Li, R., Lewis, J.H., Cervino, L.I., Jiang, S.B.: A feasibility study of markerless fluoroscopic gating for lung cancer radiotherapy using 4DCT templates. Phys. Med. Biol. 54, N489–500 (2009)CrossRef
17.
Li, R., Lewis, J.H., Jiang, S.B.: Markerless fluoroscopic gating for lung cancer radiotherapy using generalized linear discriminant analysis. In: Fourth International Conference on Machine Learning and Applications, pp. 468–472 (2009)
18.
Lin, T., Zha, H.: Riemannian manifold learning. IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008)CrossRef
19.
Lin, T., Cervino, L.I., Tang, X., Vasconcelos, N., Jiang, S.B.: Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy. Phys. Med. Biol. 54, 981–992 (2009)CrossRef
20.
Lin, T., Li, R., Tang, X., Dy, J.G., Jiang, S.B.: Markerless gating for lung cancer radiotherapy based on machine learning techniques. Phys. Med. Biol. 54, 1555–1563 (2009)CrossRef
21.
Moser, T., Biederer, J., Nill, S., Remmert, G., Bendl, R.: Detection of respiratory motion in fluoroscopic images for adaptive radiotherapy. Phys. Med. Biol. 53, 3129–3145 (2008)CrossRef
22.
Murphy, M.J., Chang, S.D., Gibbs, I.C., Le, Q.T., et al.: Patterns of patient movement during frameless image-guided radiosurgery. Int. J. Radiat. Oncol. Biol. Phys. 55, 1400–1408 (2003)CrossRef
23.
Murphy, M.J.: Tracking moving organs in real time. Semin. Radiat. Oncol. 14, 91–100 (2004)CrossRef
24.
Neicu, T., Shirato, H., Seppenwoolde, Y., Jiang, S.B.: Synchronized moving aperture radiation therapy (smart): average tumour trajectory for lung patients. Phys. Med. Biol. 48, 587–598 (2003)CrossRef
25.
Neicu, T., Berbeco, R., Wolfgang, J., Jiang, S.B.: Synchronized moving aperture radiation therapy (SMART): improvement of breathing pattern reproducibility using respiratory coaching. Phys. Med. Biol. 51, 617–636 (2006)CrossRef
26.
Ozhasoglu, C., Murphy, M.J., Glosser, G., Bodduluri, M., et al.: Real-time tracking of the tumor volume in precision radiotherapy and body radiosurgery—a novel approach to compensate for respiratory motion. In: Computer Assisted Radiology and Surgery, pp. 691–696 (2000)
27.
Papiez, L.: The leaf sweep algorithm for an immobile and moving target as an optimal control problem in radiotherapy delivery. Math. Comput. Model. 37, 735–745 (2003)MathSciNetCrossRefMATH
28.
Papiez, L., Rangaraj, D.: DMLC leaf-pair optimal control for mobile, deforming target. Med. Phys. 32, 275–285 (2005)CrossRef
29.
Rangaraj, D., Papiez, L.: Synchronized delivery of DMLC intensity modulated radiation therapy for stationary and moving targets. Med. Phys. 32, 1802–1817 (2005)CrossRef
30.
Rottmann, J., Aristophanous, M., Chen, A., Berbeco, R.: A multi-region algorithm for markerless beam’s-eye view lung tumor tracking. Phys. Med. Biol. 55, 5585–5598 (2010)CrossRef
31.
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRef
32.
Schweikard, A., Glosser, G., Bodduluri, M., Murphy, M.J., Adler, J.R.: Robotic motion compensation for respiratory movement during radiosurgery. Comput. Aided Surg. 5, 263–277 (2000)CrossRef
33.
Shirato, H., Harada, T., Harabayashi, T., Hida, K., et al.: Feasibility of insertion/implantation of 2.0-mm-diameter gold internal fiducial markers for precise setup and real-time tumor tracking in radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 56, 240–247 (2003)CrossRef
34.
Shirato, H., Shimizu, S., Kunieda, T., Kitamura, K., et al.: Physical aspects of a real-time tumor-tracking system for gated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 48, 1187–1195 (2000)CrossRef
35.
Suh, Y., Yi, B., Ahn, S., Kim, J., et al.: Aperture maneuver with compelled breath (AMC) for moving tumors: a feasibility study with a moving phantom. Med. Phys. 31, 760–766 (2004)CrossRef
36.
Tang, X., Sharp, G.C., Jiang, S.B.: Fluoroscopic tracking of multiple implanted fiducial markers using multiple object tracking. Phys. Med. Biol. 52, 4081–4098 (2007)CrossRef
37.
Tsao, A.: Lung Carcinoma: Tumors of the Lungs, Merck Manual Professional Edition (2007)
38.
Trofimov, A., Rietzel, E., Lu, H.M., Martin, B., et al.: Temporo-spatial IMRT optimization: concepts, implementation and initial results. Phys. Med. Biol. 50, 2779–2798 (2005)CrossRef
39.
Vapnik, V.N.: Statistical Learning Theory. Wiley (1998)
40.
Webb, S.: The effect on IMRT conformality of elastic tissue movement and a practical suggestion for movement compensation via the modified dynamic multileaf collimator (dmlc) technique. Phys. Med. Biol. 50, 1163–1190 (2005)CrossRef
41.
Webb, S.: Limitations of a simple technique for movement compensation via movement-modified fluence profiles. Phys. Med. Biol. 50, N155–161 (2005)CrossRef
42.
Wijesooriya, K., Bartee, C., Siebers, J.V., Vedam, S.S., Keall, P.J.: Determination of maximum leaf velocity and acceleration of a dynamic multileaf collimator: implications for 4d radiotherapy. Med. Phys. 32, 932–941 (2005)CrossRef
43.
Xu, Q., Hamilton, R.R., Alexander, B., Jiang, S.: Lung tumor tracking in fluoroscopic video based on optical flow. Med. Phys. 35, 5351–5359 (2008)CrossRef
44.
Xu, Q., Hamilton, R.J., Schowengerdt, R.A., Jiang, S.B.: A deformable lung tumor tracking method in fluoroscopic video using active shape models: a feasibility study. Phys. Med. Biol. 52, 5277–5293 (2007)CrossRef
Metadaten
Titel
Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques
verfasst von
Tong Lin
Yucheng Lin
Copyright-Jahr
2018
Buch
Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Print ISBN: 978-3-319-68842-8

Electronic ISBN: 978-3-319-68843-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-68843-5_12