Abstract
Increasing number of people are suffering from cancer these days. The highest incidence among women is breast cancer. One of the main sources of diagnostic information is Magnetic Resonance Imaging (MRI). Another one is Positron Emission Tomography with Computer Thomography (PET-CT). Both examinations take place in different patient positions (prone and supine respectively). The only way to obtain complete diagnostic information is to bring MRI images into PET-CT space and compare both. Our preliminary studies focus on creating a simple, deformable finite element model of the breast. Proposed algorithm allows to obtain breast model in supine position, created from MRI images obtained in prone position.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Azar, F.S., Metaxas, D.N., Schnall, M.D.: Methods for modelling predicting mechanical deformations of the breast under external perturbations. Med. Image Anal. 6, 1–27 (2002)
Behrens, S., Laue, H., Althaus, M., Boehler, T., Kuemmerlen, B., Hahn, H.K., Peitgen, H.O.: Computer assistance for MR based diagnosis of breast cancer: present and future challenges. Comput. Med. Imaging Graph. 31(4–5), 236–247 (2007)
Han, L., Hipwell, J., Taylor, Z., Tanner, C., Ourselin, S., Hawkes, D.J.: Fast deformation simulation of breasts using GPU-based dynamic explicit finite element method. Digit Mammo. 6136, 728–735 (2010)
Han, L., Hipwell, J., Mertzanidou, T., Carter, T., Modat, M., Ourselin, S., Hawkes, D.: A hybrid FEM-based method for aligning prone and supine images for image guided breast surgery. From Nano to Macro: 2011 IEEE International Symposium on Biomedical Imaging, pp. 1239–1242 (2011)
Han, L., Hipwell, J.H., Eiben, B., Barratt, D., Modat, M., Ourselin, S., Hawkes, D.J.: A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images. IEEE Trans. Med. Imaging. 33(3), 682–694 (2014)
Hopp, T., Dietzel, M., Baltzer, P.A., Kreisel, P., Kaiser, W.A., Gemmeke, H., Ruiter, N.V.: Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization. Med. Image Anal. 17(2), 209–218 (2012)
Lee, A.W., Rajagopal, V., Babarenda Gamage, T.P., Doyle, A.J., Nielsen, P.B., Nash, M.P.: Breast lesion co-localisation between X-ray and MR images using finite element modelling. Med. Image Anal. 17(8), 1256–1264 (2013)
Moy, M., Noz, M.E., Maguire, G.Q., Melsaether, A., Deans, A.E., Murphy-Walcott, A.D., Ponzo, F.: Role of fusion prone FDG-PET and Magnetic Resonance Imaging of the breasts in the evaluation of breast cancer. Breast J. 16(4), 369–376 (2010)
Pathmanathan, P., Gavaghan, D.J., Whiteley, J.P., Chapman, S.J., Brady, J.M.: Predicting tumor location by modeling the deformation of the breast. IEEE Trans. Biomed. Eng. 55(10), 2471–2480 (2008)
Song, H., Cui, X., Sun, F.: Breast tissue 3D segmentation and visualization on MRI. Int. J. Biomed. Im. 859746 (2013)
Srikantha, A., Harz, M.T., Newstead, G., Wang, L., Platel, B., Hegenscheid, K., Mann, R.M., Hahn, H.K., Peitgen, H.-O.: Symmetry-based detection and diagnosis of DCIS in breast MRI. Med. Imaging 2013: Comput.-Aided Diagn. 86701E (2013)
Ramiao, N., Martins, P., Fernandes, A.A.: Biomechanical properties of breast tissue. ENBENG 2013, 1–6 (2013)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–21 (1999)
Tanner, C., Hipwell, J.H., Hawkes, D.J., Szekely, G.: Breast shapes on real and simulated mammograms. Digit Mammo. 6136, 540–547 (2010)
Wang, L., Filippatos, K., Friman, O., Hahn, H.K.: Fully automated segmentation of the pectoralis muscle boundary in breast MR images. Med. Imaging 2011: Comput.-Aided Diagn. 796309 (2011)
Wessel, C., Schnabel, J.A., Brady, M.: Realistic biomechanical model of a cancerous breast for the registration of prone to supine deformations. EMBC 2013, 7249–7252 (2013)
Wu, S., Weinstein, S.P., Conant, E.F., Schnall, M.D., Kontos, D.: Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. Med. Phys. 40(4), 042301 (2013)
Acknowledgments
This work was supported by the National Science Centre NCN under Grant No. 2011/03/B/ST6/04384 (AS), the National Centre for Research and Development under grant number PBS/32/RJP8/2015/514 (DB) and the Institute of Automatic Control under Grant No. BKM-514/RAU1/2015/t.12 (MDW). This work was supported by Scholarship Program “DoktoRIS—Program stypendialny na rzecz innowacyjnego Slaska”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Danch-Wierzchowska, M., Borys, D., Swierniak, A. (2016). Breast Deformation Modeling Based on MRI Images, Preliminary Results. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-319-39904-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-39904-1_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39903-4
Online ISBN: 978-3-319-39904-1
eBook Packages: EngineeringEngineering (R0)