Abstract
In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63mm.
Chapter PDF
Similar content being viewed by others
Keywords
- Motion Vector
- Motion Estimation
- Little Square Support Vector Machine
- Lung Field
- Kernel Principal Component Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
McClelland, J.R., Blackall, J.M., Tarte, S., et al.: A Continuous 4D Motion Model from Multiple Respiratory Cycles for Use in Lung Radiotherapy. Medical Physics 33, 3348–3358 (2006)
Zhang, Q., Pevsner, A., Hertanto, A., Hu, Y.C., Rosenzweig, K.E., Ling, C.C., Mageras, G.S.: A Patient-Specific Respiratory Model of Anatomical Motion for Radiation Treatment Planning. Medical Physics 32, 4772–4782 (2007)
Sundaram, T.A., Avants, B.B., Gee, J.C.: A Dynamic Model of Average Lung Deformation Using Capacity-Based Reparameterization and Shape Averaging of Lung MR Images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 1000–1007. Springer, Heidelberg (2004)
Von Siebenthal, M., Szkely, G., Lomax, A., Cattin, P.: Inter-Subject Modeling of Liver Deformation During Radiation Therapy. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 659–666. Springer, Heidelberg (2007)
Lu, W., Song, J.H., Christensen, G.E., Parikh, P.J., Zhao, T., Hubenschmidt, J.P., Bradley, J.D., Low, D.A.: Evaluating Lung Motion Variations in Repeated 4D CT Studies Using Inverse Consistent Image Registration. International Journal of Radiation Oncology Biology Physics 66, S606–S607 (2006)
Yang, D.S., Lu, W., Low, D.A., Deasy, J.O., Hope, A.J., El Naqa, I.: 4D-CT Motion Estimation Using Deformable Image Registration and 5D Respiratory Motion Modeling. Medical Physics 35, 4577–4590 (2008)
Kim, K.I., Franz, M.O., Scholkopf, B.: Iterative Kernel Principal Component Analysis for Image Modeling. IEEE Trans. on Patt. Analy. and Mach. Intell. 27, 1351–1366 (2005)
An, S.J., Liu, W.Q., Venkatesh, S.: Fast Cross-Validation Algorithms for Least Squares Support Vector Machine and Kernel Ridge Regression. Pattern Recognition 40, 2154–2162 (2007)
He, T., Xue, Z., Wong, K., Valdivia y Alvarado, M., Zhang, Y., Xie, W., Wong, S.T.C.: Minimally Invasive Multimodality Image-Guided (MIMIG) Molecular Imaging System for Peripharal Lung Cancer Intervention and Diagnosis. In: Navab, N., Jannin, P. (eds.) IPCAI 2010. LNCS, vol. 6135, Springer, Heidelberg (2010)
Xue, Z., Shen, D.G., Davatzikos, C.: CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing. Neuroimage 30, 388–399 (2006)
Xue, Z., Wong, K., Wong, S.T.C.: Joint Registration and Segmentation of Serial Lung CT Images for Image-Guided Lung Cancer Diagnosis and Therapy. Computerized Medical Imaging and Graphics 34, 55–60 (2010)
Kwok, J.T.Y., Tsang, I.W.H.: The Pre-Image Problem in Kernel Methods. IEEE Transactions on Neural Networks 15, 1517–1525 (2004)
Xue, Z., Shen, D., Karacali, B., Stern, J., Rottenberg, D., Davatzikos, C.: Simulating Deformations of MR Brain Images for Validation of Atlas-Based Segmentation and Registration Algorithms. Neuroimage 33, 855–866 (2006)
Gerig, G., Jomier, M., Chakos, M.: Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 516–528. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, T., Xue, Z., Xie, W., Wong, S.T.C. (2010). Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals . In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15711-0_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-15711-0_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15710-3
Online ISBN: 978-3-642-15711-0
eBook Packages: Computer ScienceComputer Science (R0)