Abstract
This study aimed to determine the accuracy to which prone and supine MRI can be registered using biomechanical models, to help clinicians with pre-operative breast cancer treatment planning. A framework was developed for constructing a biomechanical model of the breast from diagnostic prone MRI. These images were then warped using a prone-to-supine mechanics simulation to generate an initial estimate of the tissue displacement in the supine position. This initial estimate of tissue motion was subsequently used for registering the prone MRI to pre-operative supine MRI using an image intensity-based non-rigid registration algorithm. The framework was demonstrated using MR images from two volunteers. The results showed that a two parameter breast model provided good initial estimates of tissue displacement for registering the prone and supine MR images, with registration errors less than 5 mm for mean tissue displacement magnitudes of up to 61.8 mm between the prone and supine positions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pleijhuis RG et al (2009) Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions. Ann Surg Oncol 16(10):2717–2730
Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32(7):1153–1190
Rueckert D et al (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721
Schnabel J et al (2001) A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: Niessen W, Viergever M (eds) Medical image computing and computer-assisted intervention 2001. Springer, Berlin Heidelberg, pp 573–581
Lee AWC et al (2010) Breast image registration by combining finite elements and free-form deformations. In: Martí J et al (eds) Digital mammography. Springer, Berlin/Heidelberg, pp 736–743
Hipwell JH et al (2016) A review of biomechanically informed breast image registration. Phys Med Biol 61(2):R1
Han L et al (2014) A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images. IEEE Trans Med Imaging 33(3):682–694
Babarenda Gamage TP et al (2012) Patient-specific modeling of breast biomechanics with applications to breast cancer detection and treatment. In: Gefen A (ed) Patient-specific modeling in tomorrow’s medicine. Springer, Berlin Heidelberg, pp 379–412
Bradley C et al (2011) OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. Prog Biophys Mol Biol 107(1):32–47
Eiben B, Vavourakis V, Hipwell JH, Kabus S, Lorenz C, Buelow T, Hawkes DJ (2014) Breast deformation modelling: comparison of methods to obtain a patient specific unloaded configuration. In: Proceedings of SPIE 9036, Medical Imaging 2014: image-guided procedures, robotic interventions, and modeling, pp 903615-1–903615-8. doi:10.1117/12.2043607
Rajagopal V et al (2007) Determining the finite elasticity reference state from a loaded configuration. Int J Numer Methods Eng 72(12):1434–1451
Lee AWC (2011) Breast image fusion using biomechanics. PhD thesis, University of Auckland
Johns PC, Yaffe MJ (1987) X-ray characterisation of normal and neoplastic breast tissues. Phys Med Biol 32(6):675–695
Urbanchek MG et al (2001) Specific force deficit in skeletal muscles of old rats is partially explained by the existence of denervated muscle fibers. J Gerontol A Biol Sci Med Sci 56(5):B191–B197
Schnabel JA et al (2003) Validation of nonrigid image registration using finite-element methods: application to breast MR images. IEEE Trans Med Imaging 22(2):238–247
Babarenda Gamage TP et al (2012) Modeling prone to supine breast deformation under gravity loading using heterogeneous finite element models. In: Nielsen PMF, Wittek A, Miller K (eds) Computational biomechanics for medicine. Springer, New York, pp 29–38
Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921
Garlapati RR et al (2015) Towards measuring neuroimage misalignment. Comput Biol Med 64:12–23
Acknowledgements
The authors are grateful for financial support from the New Zealand Government Ministry for Business, Innovation and Employment (MBIE) and the University of Auckland Foundation. The authors thank Duane Malcolm for his contributions to this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gamage, T.P.B., Baluwala, H.Y., Nash, M.P., Nielsen, P.M.F. (2017). Registration of Prone and Supine Breast MRI for Breast Cancer Treatment Planning. In: Wittek, A., Joldes, G., Nielsen, P., Doyle, B., Miller, K. (eds) Computational Biomechanics for Medicine. Springer, Cham. https://doi.org/10.1007/978-3-319-54481-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-54481-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54480-9
Online ISBN: 978-3-319-54481-6
eBook Packages: EngineeringEngineering (R0)