Abstract
In respiratory motion modeling for the liver, the breathing pattern is usually obtained by using special tracking devices from skin or diaphragm, and subsequently applied as input to a 4D motion model for motion estimation. However, due to the intrinsic limits and economical costs of these tracking devices, the identification of the breathing pattern directly from intra-operative ultrasound images is a more attractive option. In this paper, a new method is proposed to automatically track the breathing pattern from 2D ultrasound image sequences of the liver. The proposed method firstly utilizes a Hessian matrix-based 2D line filter to identify the liver boundary, then uses an adaptive search strategy to in real-time match a template block centered inside the identified boundary, and consequently extract the translational motion of the boundary as the respiratory pattern. The experiments on four volunteers demonstrate that the respiratory pattern extracted by our method is highly consistent to those acquired by an EM tracking system with the correlation coefficient of at least 0.91.
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References
Ho, H., Yuen, J.S.P., Cheng, C.W.S.: Robotic prostate biopsy and its relevance to focal therapy of prostate cancer. Nature Reviews Urology 8, 579–585 (2011)
Bruder, R., Ernst, F., Schlaefer, A., Schweikard, A.: A Framework for Real-Time Target Tracking in Radiosurgery using Three-dimensional Ultrasound. In: CARS 2011, pp. S306–S307 (2011)
Nadeau, C., Krupa, A., Gangloff, J.: Automatic Tracking of an Organ Section with an Ultrasound Probe: Compensation of Respiratory Motion. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 57–64. Springer, Heidelberg (2011)
McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: A review. Medical Image Analysis 17, 19–42 (2012)
Rohlfing, T., Maurer, C.R., O’Dell, W.G., Zhong, J.: Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. Medical Physics 31, 427–432 (2004)
Preiswerk, F., Arnold, P., Fasel, B., Cattin, P.C.: Robust tumour tracking from 2D imaging using a population-based statistical motion model. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 209–214 (2012)
Rijkhorst, E.-J., Rivens, I., ter Haar, G., Hawkes, D., Barratt, D.: Effects of Respiratory Liver Motion on Heating for Gated and Model-Based Motion-Compensated High-Intensity Focused Ultrasound Ablation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 605–612. Springer, Heidelberg (2011)
Khamene, A., et al.: Characterization of Internal Organ Motion Using Skin Marker Positions. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 526–533. Springer, Heidelberg (2004)
Ernst, F., Martens, V., Schlichting, S., Beširević, A., Kleemann, M., Koch, C., Petersen, D., Schweikard, A.: Correlating Chest Surface Motion to Motion of the Liver Using ε-SVR – A Porcine Study. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 356–364. Springer, Heidelberg (2009)
von Siebenthal, M.: Analysis and modelling of respiratory liver motion using 4DMRI. Ph.D. thesis, ETH Zurich (2008)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Wu, J., Li, C., Huang, S., Liu, F., Tan, B.S., Ooi, L.L., Yu, H., Liu, J.: Fast and robust extraction of surrogate respiratory signal from intra-operative liver ultrasound images. International Journal of Computer Assisted Radiology and Surgery (publish online, 2013)
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Wu, J. et al. (2013). Automatic and Real-Time Identification of Breathing Pattern from Ultrasound Liver Images. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds) Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. MIAR AE-CAI 2013 2013. Lecture Notes in Computer Science, vol 8090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40843-4_4
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DOI: https://doi.org/10.1007/978-3-642-40843-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40842-7
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