Abstract
Computed Tomography (CT) has been widely used in image-guided procedures such as intervention and radiotherapy of lung cancer. However, due to poor reproducibility of breath holding or respiratory cycles, discrepancies between static images and patient’s current lung shape and tumor location could potentially reduce the accuracy for image guidance. Current methods are either using multiple intra-procedural scans or monitoring respiratory motion with tracking sensors. Although intra-procedural scanning provides more accurate information, it increases the radiation dose and still only provides snapshots of patient’s chest. Tracking-based breath monitoring techniques can effectively detect respiratory phases but have not yet provided accurate tumor shape and location due to low dimensional signals. Therefore, estimating the lung motion and generating dynamic CT images from real-time captured high-dimensional sensor signals acts as a key component for image-guided procedures. This paper applies a principal component analysis (PCA)-based statistical model to establish the relationship between lung motion and chest surface motion from training samples, on a template space, and then uses this model to estimate dynamic images for a new patient from the chest surface motion. Qualitative and quantitative results showed that the proposed high-dimensional estimation algorithm yielded more accurate 4D-CT compared to fiducial marker-based estimation.
Chapter PDF
References
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)
Vandemeulebroucke, J., Rit, S., Kybic, J., Clarysse, P., Sarrut, D.: Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med. Phys. 38, 166–178 (2011)
Handels, H., Werner, R., Schmidt, R., Frenzel, T., Lu, W., Low, D., Ehrhardt, J.: 4D medical image computing and visualization of lung tumor mobility in spatio-temporal CT image data. Int. J. Med. Inform. 76(suppl. 3), S433–S439 (2007)
Wu, G., Wang, Q., Lian, J., Shen, D.: Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 532–539. Springer, Heidelberg (2011)
Klinder, T., Lorenz, C., Ostermann, J.: Prediction framework for statistical respiratory motion modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 327–334. Springer, Heidelberg (2010)
Ehrhardt, J., Werner, R., Schmidt-Richberg, A., Handels, H.: Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans. Med. Imag. 30, 251–265 (2011)
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. Int. J. Radiat. Oncol. 66, S606–S607 (2006)
Santelli, C., Nezafat, R., Goddu, B., Manning, W.J., Smink, J., Kozerke, S., Peters, D.C.: Respiratory Bellows Revisited for Motion Compensation: Preliminary Experience for Cardiovascular MR. Magnet. Reson. Med. 65, 1098–1103 (2011)
He, T., Xue, Z., Xie, W., Wong, S.T.C.: 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.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 392–399. Springer, Heidelberg (2010)
He, T., Xue, Z., Lu, K., Valdivia y Alvarado, M., Wong, K.K., Xie, W., Wong, S.T.: A minimally invasive multimodality image-guided (MIMIG) system for peripheral lung cancer intervention and diagnosis. Comput. Med. Img. Grap. 36, 345–355 (2012)
Tan, K.S., Saatchi, R., Elphick, H., Burke, D.: Real-time vision based respiration monitoring system. In: 7th International Symposium on Communication Systems Networks and Digital Signal Processing, pp. 770–774 (2010)
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. Comput. Med. Img. Grap. 34, 55–60 (2010)
Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imag. 20, 1131–1139 (2001)
Davatzikos, C., Shen, D., Mohamed, A., Kyriacou, S.K.: A framework for predictive modeling of anatomical deformations. IEEE Trans. Med. Imag. 20, 836–843 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
He, T., Xue, Z., Yu, N., Nitsch, P.L., Teh, B.S., Wong, S.T. (2014). Estimating Dynamic Lung Images from High-Dimension Chest Surface Motion Using 4D Statistical Model. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)