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Patient-Specific Modeling of Breast Biomechanics with Applications to Breast Cancer Detection and Treatment

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Patient-Specific Modeling in Tomorrow's Medicine

Abstract

There are many challenges clinicians are faced with when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians by providing a physics based approach to addressing some of these challenges. This review describes the state of the art in the field, from aiding co-location of information between various medical imaging modalities used to identify tumours; to providing the ability to predict the location of these tumors during different biopsy or surgical procedures; to aiding temporal registration of follow-up medical images used to review the progress of suspicious lesions and therefore evaluate effectiveness of breast cancer treatments; to aiding implant selection for breast augmentation procedures and the subsequent prediction of the resulting appearance following such procedures. Significant technical challenges remain in terms of improving the accuracy of such biomechanical models. These include the precise determination and application of loading and boundary constraints applied during different clinical procedures, and accurate characterization of individual-specific mechanical properties of the different breast tissues. In addition to these more technical challenges, a number of practical challenges exist when translating biomechanical models from research based environments into clinical workflows, which demand general applicability, and ease and speed of use. This review outlines such challenges and provides an overview of the steps researchers are taking to address them. Once these challenges have been met, there is potential for extending the use of biomechanics to simulate more complex clinical procedures, from modeling needle insertions into breast tissue during real-time biopsy procedures, to simulating and predicting the outcome of different surgical procedures such as tumorectomies. Clinical adoption of such state-of-the-art modeling techniques has significant potential for reducing the number of misdiagnosed breast cancers while also helping improve clinical treatment of patients.

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References

  1. Agache, P., Humbert, P.: Measuring the Skin. Editions Mdicales Internationales, Paris 2000, (2004)

    Google Scholar 

  2. Arruda, E.M., Boyce, M.C.: A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials. J. Mech. Phys. Solids 41(2), 389–412 (1993)

    Article  Google Scholar 

  3. Azar, F.S., Metaxas, D.N., Schnall, M.D.: A finite element model of the breast for predicting mechanical deformations during biopsy procedures. In: Mathematical Methods in Biomedical Image Analysis, 2000. Proceedings. IEEE Workshop on, pp. 38–45 (2000)

    Google Scholar 

  4. Azar, F.S., Metaxas, D.N., Schnall, M.D.: A deformable finite element model of the breast for predicting mechanical deformations under external perturbations. Acad. Radiol. 8(10), 965–975 (2001)

    Article  Google Scholar 

  5. Azar, F.S., Metaxas, D.N., Schnall, M.D.: Methods for modeling and predicting mechanical deformations of the breast under external perturbations. Med. Image. Anal. 6(1), 1–27 (2002)

    Article  Google Scholar 

  6. Barbone, P.E., Oberai, A.A.: A review of the mathematical and computational foundations of biomechanical imaging. In: De, S., Guilak, F., Mofrad, R.K.M. (eds.) Computational Modeling in Biomechanics, pp. 375–408. Springer, The Netherlands (2010)

    Chapter  Google Scholar 

  7. Baumann, R., Glauser, D., Tappy, D., Baur, C., Clavel, R.: Force feedback for virtual reality based minimally invasive surgery simulator. Stud. Health Technol. Inform. 29, 564–579 (1996)

    Google Scholar 

  8. Behrenbruch, C.P., Marias, K., Armitage, P.A., Yam, M., Moore, N.R., English, R.E., Clarke, P.J., Leong, F.J., Brady, J.M.: Fusion of contrast-enhanced breast mr and mammographic imaging data. Br. J. Radiol. 77(Spec No 2), S201–S208 (2004)

    Article  Google Scholar 

  9. Berardesca, E., Elsner, P., Wilhelm, K., Maibach, H.: Bioengineering of the Skin: Methods and Instrumentation. CRC Press, Boca Raton (1995)

    Google Scholar 

  10. Berman, C.G.: Recent advances in breast-specific imaging. Cancer Control 14(4), 338–349 (2007)

    Google Scholar 

  11. Blumgart, E.I., Uren, R.F., Nielsen, P.M.F., Nash, M.P., Reynolds, H.M.: Lymphatic drainage and tumour prevalence in the breast: a statistical analysis of symmetry, gender and node field independence. J. Anat. 218(6), 652–659 (2011)

    Article  Google Scholar 

  12. Bonet, J., Wood, R.: Nonlinear Continuum Mechanics for Finite Element Analysis. Cambridge University Press, Cambridge (1997)

    MATH  Google Scholar 

  13. Boyce, M.C., Arruda, E.M.: Constitutive models of rubber elasticity: a review. Rubber Chem. Technol. 73, 504–523 (2000)

    Article  Google Scholar 

  14. Boyle, P., Levin, B.: World Cancer Report 2008. International Agency for Research on Cancer (2008)

    Google Scholar 

  15. Bradley, C.P., Pullan, A.J., Hunter, P.J.: Geometric modeling of the human torso using cubic hermite elements. Ann. Biomed. Eng. 25(1), 96–111 (1997)

    Article  Google Scholar 

  16. Carr, J., Fright, W., Beatson, R.: Surface interpolation with radial basis functions for medical imaging. IEEE Trans. Med. Imaging 16(1), 96–107 (1997)

    Article  Google Scholar 

  17. Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R.: Reconstruction and representation of 3d objects with radial basis functions. In: Proceedings of the 28th Annual Conference on Computer graphics and Interactive Techniques, ACM, New York, NY, USA, SIGGRAPH ’01, pp. 67–76 (2001)

    Google Scholar 

  18. Carter, T., Tanner, C., Crum, W., Beechey-Newman, N., Hawkes, D.: Medical imaging and augmented reality 3rd International Workshop, Shanghai, China, 17–18 August, 2006 Proceedings, Springer Berlin, Heidelberg, Chap A Framework for Image-Guided Breast Surgery, pp. 203–210 (2006)

    Google Scholar 

  19. Carter, T., Tanner, C., Beechey-Newman, N., Barratt, D., Hawkes, D.: Mr navigated breast surgery: method and initial clinical experience. Med. Image Comput. Comput. Assist. Interv. 11, 356–363 (2008)

    Google Scholar 

  20. Carter, T.J., Sermesant, M., Cash, D.M., Barratt, D.C., Tanner, C., Hawkes, D.J.: Application of soft tissue modelling to image-guided surgery. Med. Eng. Phys. 27(10), 893–909 (2005)

    Article  Google Scholar 

  21. Carter T.J., Tanner C., Hawkes D.J.: Determining material properties of the breast for image-guided surgery. In: Miga M.I., Wong K.H. (eds.) Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling. SPIE 7261:726124 (2009)

    Google Scholar 

  22. Chung, J., Rajagopal, V., Nielsen, P.M.F., Nash, M.P.: A biomechanical model of mammographic compressions. Biomech. Model. Mechanobiol. 7, 43–52 (2008)

    Article  Google Scholar 

  23. Chung, J.H.: Modelling mammographic mechanics. PhD thesis, The University of Auckland (2008)

    Google Scholar 

  24. Chung, J.H., Rajagopal, V., Laursen A Tod., Nielsen, P.M.F., Nash, M.P.: Frictional contact mechanics methods for soft materials: Application to tracking breast cancers. J. Biomech. 41, 69–77 (2008)

    Article  Google Scholar 

  25. Chung, J.H., Rajagopal, V., Nielsen, P.M.F., Nash, M.P.: Modelling mammographic compression of the breast, Medical Image Computing and Computer-assisted Intervention MICCAI 2008, Lecture Notes in Computer Science, Chap Modelling Mammographic Compression of the Breast, vol. 5242/2008, pp. 758–765. Springer, Heidelberg (2008)

    Google Scholar 

  26. Cooper, A.P.: On the Anatomy of the Breast. Longman Publishing, London (1840)

    Google Scholar 

  27. Cotin, S., Delingette, H., Ayache, N.: A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. Visual Comput. 16, 437–452 (2000)

    Article  MATH  Google Scholar 

  28. Dawant, B.: Non-rigid registration of medical images: purpose and methods, a short survey. In: IEEE, International Symposium on Biomedical Imaging, pp. 465–468 (2002)

    Google Scholar 

  29. Dehghani, H., Doyley, M.M., Pogue, B.W., Jiang, S., Geng, J., Paulsen, K.D.: Breast deformation modelling for image reconstruction in near infrared optical tomography. Phys. Med. Biol. 49(7), 1131 (2004)

    Article  Google Scholar 

  30. Elsner, P., Berardesca, E., Wilhelm, K., Maibach, H.: Bioengineering of the Skin. CRC Press, Boca Raton (2002)

    Google Scholar 

  31. Fernandez, J.W., Mithraratne, P., Thrupp, S.F., Tawhai, M.H., Hunter, P.J.: Anatomically based geometric modelling of the musculo-skeletal system and other organs. Biomech. Model. Mechanobiol. 2(3), 139–155 (2004)

    Article  Google Scholar 

  32. Fung, Y.C.: Biomechanics Mechanical Properties of Living Tissue. Springer, Berlin (1993)

    Google Scholar 

  33. Gefen, A., Dilmoney, B.: Mechanics of the normal woman’s breast. Technol. Health Care 15(4), 259–271 (2007)

    Google Scholar 

  34. Guinebretiere, J., Menet, E., Tardivon, A., Cherel, P., Vanel, D.: Normal and pathological breast, the histological basis. Eur. J. Radiol. 54(1), 6–14 (2005)

    Article  Google Scholar 

  35. Guo, Y., Sivaramakrishna, R., Lu, C.C., Suri, J., Laxminarayan, S.: Breast image registration techniques: a survey. Med. Biol. Eng. Comput. 44, 15–26 (2006)

    Article  Google Scholar 

  36. Haerle, F., Champy, M., Terry, B.: Atlas of Craniomaxillofacial Osteosynthesis: Microplates, Miniplates and Screws, edn. 2. Thieme, Stuttgart (2009)

    Google Scholar 

  37. Han, L., Burcher, M., Noble, J.: Non-invasive measurement of biomechanical properties of in vivo soft tissues. In: Dohi, T., Kikinis, R. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2002, Lecture Notes in Computer Science, vol. 2488, pp. 208–215. Springer, Heidelberg (2002)

    Google Scholar 

  38. Han, L., Hipwell, J.H., Taylor, Z.A., Tanner, C., Ourselin, S., Hawkes, D.J.: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16–18, 2010. Proceedings, Springer Berlin, Heidelberg, Chap Fast Deformation Simulation of Breasts Using GPU-Based Dynamic Explicit Finite Element Method. Lecture Notes in Computer Science, pp. 728–735 (2010)

    Google Scholar 

  39. Hawkes, D., Barratt, D., Blackall, J., Chan, C., Edwards, P., Rhode, K., Penney, G., McClelland, J., Hill, D.: Tissue deformation and shape models in image-guided interventions: a discussion paper. Med. Image Anal. 9(2), 163–175 (2005)

    Article  Google Scholar 

  40. Hindle, W.H. (ed.): Breast Care: A Clinical Guidebook for Women’s Primary Healthcare Providers. Springer, New York (1999)

    Google Scholar 

  41. Hipwell, J.H., Tanner, C., Crum, W.R., Schnabel, J.A., Hawkes, D.J.: A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression. IEEE Trans. Med. Imaging 26(9), 1190–1200 (2007)

    Article  Google Scholar 

  42. Huang, S.Y., Boone, J.M., Yang, K., Kwan, A.L.C., Packard, N.J.: The effect of skin thickness determined using breast ct on mammographic dosimetry. Med. Phys. 35(4), 1199–1206 (2008)

    Article  Google Scholar 

  43. Hudson, D.A.: Factors determining shape and symmetry in immediate breast reconstruction. Ann. Plast. Surg. 52(1), 15–21 (2004)

    Article  MathSciNet  Google Scholar 

  44. Humphrey, J.: Review paper: continuum biomechanics of soft biological tissues. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences 459(2029), 3–46 (2003)

    Google Scholar 

  45. Hunter, P., Nielsen, P.: A strategy for integrative computational physiology. Physiology 20(5), 316–325 (2005)

    Article  Google Scholar 

  46. Hunter, P.J.: The IUPS physiome project: a framework for computational physiology. Prog. Biophys. Mol. Biol. 85, 551–569 (2004)

    Article  Google Scholar 

  47. Jaccard, P.: The distribution of the flora in the alpine zone. 1. New Phytologist 11(2), 37–50 (1912)

    Article  Google Scholar 

  48. Jiang, L., Zhan, W., Loew, M.H.: Modeling static and dynamic thermography of the human breast under elastic deformation. Phys. Med. Biol. 56(1), 187 (2011)

    Article  Google Scholar 

  49. Kahan, Z.: Breast Cancer, a Heterogeneous Disease Entity The Very Early Stages. 1st edn. Springer, Berlin (2011)

    Book  Google Scholar 

  50. Kellner, A.L., Nelson, T.R., Cervino, L.I., Boone, J.M.: Simulation of mechanical compression of breast tissue. IEEE Trans. Biomed. Eng. 54(10), 1885–1891 (2007)

    Article  Google Scholar 

  51. Kerdok, A.E., Jordan, P., Liu, Y., Wellman, P.S., Socrate, S., Howe, R.D.: Identification of nonlinear constitutive law parameters of breast tissue. In: Proceeding of 2005 summer Bioengineering Conference, June 22–26, Vail Cascade Resort and Spa, Vail, Colorado (2005)

    Google Scholar 

  52. Kerdok, A.E., Ottensmeyer, M.P., Howe, R.D.: Effects of perfusion on the viscoelastic characteristics of liver. J. Biomech. 39(12), 2221–2231 (2006)

    Article  Google Scholar 

  53. Kita, Y., Highnam, R., Brady, M.: Correspondence between different view breast X-rays using a simulation of breast deformation. In: Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, pp. 700–707 (1998)

    Google Scholar 

  54. Kita, Y., Highnam, R., Brady, M.: Correspondence between different view breast x rays using curved epipolar lines. Comput. Vision Image Understanding 83(1), 38–56 (2001)

    Article  MATH  Google Scholar 

  55. Kita, Y., Tohno, E., Highnam, R.P., Brady, M.: A cad system for the 3d location of lesions in mammograms. Med. Image Anal. 6(3), 267–273 (2002)

    Article  Google Scholar 

  56. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V.: Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration. NeuroImage 46(3), 786–802 (2009)

    Article  Google Scholar 

  57. Krouskop, T.A., Wheeler, T.M., Kallel, F., Garra, B.S., Hall, T.: Elastic moduli of breast and prostate tissues under compression. Ultrason. Imaging 20(4), 260–274 (1998)

    Google Scholar 

  58. Krouskop, T.A., Younes, P.S., Srinivasan, S., Wheeler, T., Ophir, J.: Differences in the compressive stress-strain response of infiltrating ductal carcinomas with and without lobular features–implications for mammography and elastography. Ultrason. Imaging 25(3), 162–170 (2003)

    Google Scholar 

  59. Krynyckyi, B.R., Kim, C.K., Goyenechea, M.R., Chan, P.T., Zhang, Z.Y., Machac, J.: Clinical breast lymphoscintigraphy: optimal techniques for performing studies, image atlas, and analysis of images. Radiographics 24(1), 121–145 (2004)

    Article  Google Scholar 

  60. Kuhnapfel, U., Cakmak, H.K., Maass, H.: Endoscopic surgery training using virtual reality and deformable tissue simulation. Comput. Graphics 24(5), 671–682 (2000)

    Article  Google Scholar 

  61. Langer, K.: On the anatomy and physiology of the skin: I the cleavability of the cutis. Br. J. Plast. Surg. 31(1), 3–8 (1978)

    Article  Google Scholar 

  62. Laursen, T.A.: Computational Contact and Impact Mechanics, Fundamentals of Modeling Interfacial Phenomena in Nonlinear Finite Element Analysis. 1st edn. Springer, Berlin (2002)

    MATH  Google Scholar 

  63. Lee, A.W., Rajagopal, V., Chung, J.H., Nielsen, P.M., Nash, M.P.: Method for validating breast compression models using normalised cross-correlation. In: Miller, K., Nielsen, P.M. (eds.) Computational Biomechanics for Medicine, pp. 63–71. Springer, New York (2010)

    Chapter  Google Scholar 

  64. Lee, A.W.C., Schnabel, J.A., Rajagopal, V., Nielsen, P.M.F., Nash, M.P.: Breast image registration by combining finite elements and free-form deformations. In: Mart, J., Oliver, A., Freixenet, J., Mart, R. (eds.) Digital Mammography, Lecture Notes in Computer Science, vol. 6136, pp. 736–743. Springer, Heidelberg (2010)

    Google Scholar 

  65. Lehman, C.D.: Role of mri in screening women at high risk for breast cancer. J. Magn. Reson. Imaging 24(5), 964–970 (2006)

    Article  Google Scholar 

  66. Lim, K., Chew, C., Chen, P., Jeyapalina, S., Ho, H., Rappel, J., Lim, B.: New extensometer to measure in vivo uniaxial mechanical properties of human skin. J. Biomech. 41(5), 931–936 (2008)

    Article  Google Scholar 

  67. Liu, Y., Kerdok, A.E., Howe, R.D.: International Symposium, ISMS 2004, Cambridge, MA, USA, 17–18 June 2004. Proceedings Chap A Nonlinear Finite Element Model of Soft Tissue Indentation, Lecture Notes in Computer Science, pp. 67–76. Springer, Heidelberg (2004)

    Google Scholar 

  68. Malur, S., Wurdinger, S., Moritz, A., Michels, W., Schneider, A.: Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography. Breast Cancer Res. 3, 1–6 (2000)

    Google Scholar 

  69. Mollemans, W., Schutyser, F., Van Cleynenbreugel, J., Suetens, P.: Tetrahedral mass spring model for fast soft tissue deformation. In: Ayache, N., Delingette, H. (eds.) Surgery Simulation and Soft Tissue Modeling, Lecture Notes in Computer Science, vol. 2673, pp. 1002–1003. Springer Berlin, Heidelberg (2003)

    Google Scholar 

  70. Nash, M.P., Hunter, P.J.: Computational mechanics of the heart. J. Elast. 61(1), 113–141 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  71. Nielsen, P.M.F.: The anatomy of the heart: a finite element model. PhD thesis, University of Auckland (1987)

    Google Scholar 

  72. O’Hagan, J.J., Samani, A.: Measurement of the hyperelastic properties of tissue slices with tumour inclusion. Phys. Med. Biol. 53(24), 7087 (2008)

    Article  Google Scholar 

  73. O’Hagan, J.J., Samani, A.: Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples. Phys. Med. Biol. 54(8), 2557 (2009)

    Article  Google Scholar 

  74. del Palomar, A.P., Calvo, B., Herrero, J., Lopez, J., Doblar, M.: A finite element model to accurately predict real deformations of the breast. Med. Eng. Phys. 30(9), 1089–1097 (2008)

    Article  Google Scholar 

  75. Pass, H.A.: Benign and malignant diseases of the breast. In: Norton, J.A., Barie, P.S., Bollinger, R.R., Chang, A.E., Lowry, S.F., Mulvihill, S.J., Pass, H.I., Thompson, R.W. (eds.) Surgery, pp. 2005–2035 Springer, New York (2008)

    Chapter  Google Scholar 

  76. Pathmanathan, P., Gavaghan, D.J., Whiteley, J.P., Rajagopal, V., Nielsen, P.M.F., Nash, M.P.: Predicting tumour location by simulating large deformations of the breast using a 3d finite element model and nonlinear elasticity. Medical Image Computing and Computer-Assisted Intervention MICCAI 2004, Lecture Notes in Computer Science, vol. 3217 pp. 217-224. Springer-Verlag Berlin Heidelberg (2004)

    Google Scholar 

  77. 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)

    Article  Google Scholar 

  78. Pope, T.L., Read, M.E., Medsker, T., Buschi, A.J., Brenbridge, A.N.: Breast skin thickness: normal range and causes of thickening shown on film-screen mammography. J. Can. Assoc. Radiol. 35(4), 365–368 (1984)

    Google Scholar 

  79. Qiu, Y.: Three dimensional finite element model for lesion correspondence in breast imaging. Master’s thesis, University of South Florida (2003)

    Google Scholar 

  80. Qiu, Y.: Temporal registration of mammograms by finite element simulation of mr breast volume deformation. PhD thesis, University of South Florida (2009)

    Google Scholar 

  81. Qiu, Y., Goldgof, D.B., Li, L., Sarkar, S., Zhang, Y., Anton, S.: Correspondence recovery in 2-view mammography. In: Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 15–18 April 2004, IEEE, pp. 197–200 (2004)

    Google Scholar 

  82. Qiu, Y., Sun, X., Manohar, V., Goldgof, D.: Towards registration of temporal mammograms by finite element simulation of mr breast volumes. In: Miga MI, Cleary KR (eds.) Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, SPIE, vol. 6918, p. 69182F (2008)

    Google Scholar 

  83. Rajagopal, V., Chung, J., Nielsen, P.M.F., Nash, M.P.: Finite element modelling of breast biomechanics: Finding a reference state. Conf. Proc. IEEE Eng. Med. Biol. Soc. 3, 3268–3271 (2005)

    Google Scholar 

  84. Rajagopal, V., Chung, J., Nielsen, P.M.F., Nash, M.P.: Finite element modelling of breast biomechanics: directly calculating the reference state. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 420–423 (2006)

    Article  Google Scholar 

  85. Rajagopal, V., Chung, J., Nielsen, P.M.F., Nash, M.P.: Determining the finite elasticity reference state from a loaded configuration. Int. J. Numer. Methods Eng. 72, 1434–1451 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  86. Rajagopal, V., Lee, A., Chung, J.H., Warren, R., Highnam, R.P., Nielsen, P.M.F., Nash, M.P.: Towards tracking breast cancer across medical images using subject-specific biomechanical models. Med. Image Comput. Comput. Assist. Interv. 10, 651–658 (2007)

    Google Scholar 

  87. Rajagopal, V., Lee, A., Chung, J.H., Warren, R., Highnam, R.P., Nash, M.P., Nielsen, P.M.: Creating individual-specific biomechanical models of the breast for medical image analysis. Acad. Radiol. 15(11), 1425–1436 (2008)

    Article  Google Scholar 

  88. Rajagopal, V., Nash, M.P., Highnam P, Ralph., Nielsen, P.M.F.: The breast biomechanics reference state for multi-modal image analysis. International Workshop on Digital Mammography IWDM 2008, Lecture Notes in Computer Science, vol. 5116, pp. 385–392. Springer-Verlag Berlin Heidelberg (2008)

    Google Scholar 

  89. Rajagopal, V., Chung, J.H., Highnam, R.P., Warren, R., Nielsen, P.M., Nash, M.P.: Mapping microcalcifications between 2d mammograms and 3d mri using a biomechanical model of the breast. In: Miller, K, Nielsen P.M. (eds.) Computational Biomechanics for Medicine, pp.17–28. Springer, New York (2010)

    Chapter  Google Scholar 

  90. Rajagopal, V., Nielsen, P.M.F., Nash, M.P.: Modeling breast biomechanics for multi-modal image analysis-successes and challenges. Wiley Interdisciplinary Rev.: Syst. Biol. Med. 2(3), 293–304 (2010)

    Article  Google Scholar 

  91. Reynolds, H.M., Puthran, J., Doyle, A., Jones, W., Nielsen, P.M., Nash, M.P., Rajagopal, V.: Mapping breast cancer between clinical X-ray and mr images. In: Computational Biomechanics for Medicine V, MICCAI 2010 Workshop pp.78-88, 24 September (2010)

    Google Scholar 

  92. Riggio, E., Quattrone, P., Nava, M.: Anatomical study of the breast superficial fascial system: the inframammary fold unit. Eur. J. Plast. Surg. 23(6), 310–315 (2000)

    Article  Google Scholar 

  93. Rohlfing, T., Maurer, C.R., Bluemke, D.A., Jacobs, M.A.: An alternating-constraints algorithm for volume-preserving non-rigid registration of contrast-enhanced MR breast images. In: Gee, J.C., Maintz, J.B.A., Vannier M.W. (eds.) Biomedical Image Registration – 2nd International Workshop, WBIR 2003, Philadelphia, PA, USA, June 23–24, 2003. Lecture Notes in Computer Science, vol. 2717, pp. 291–300. Springer, Heidelberg (2003a)

    Google Scholar 

  94. Rohlfing, T., Maurer J, C.R., Bluemke, D., Jacobs, M.: Volume-preserving nonrigid registration of mr breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22(6), 730–741 (2003b)

    Article  Google Scholar 

  95. Roose, L., Maerteleire, W.D., Mollemans, W., Suetens, P.: Validation of different soft tissue simulation methods for breast augmentation. Int. Congr. Ser. 1281, 485–490 (2005)

    Article  Google Scholar 

  96. Roose, L., De Maerteleire, W., Mollemans, W., Maes, F., Suetens, P.: Simulation of soft-tissue deformations for breast augmentation planning. In: Harders, M., Szkely, G. (eds.) Biomedical Simulation, Lecture Notes in Computer Science, vol. 4072, pp. 197–205. Springer, Heidelberg (2006)

    Google Scholar 

  97. Roose, L., Maerteleire, W.D., Mollemans, W., Maes, F., Suetens, P.: Pre-operative simulation and post-operative validation of soft-tissue deformations for breast implantation planning. In: Cleary, K.R., Galloway, R.L., Jr (eds.) Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display, SPIE, vol. 6141, p. 61410Z (2006)

    Google Scholar 

  98. Roose, L., Mollemans, W., Loeckx, D., Maes, F., Suetens, P.: Biomechanically based elastic breast registration using mass tensor simulation. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2006, Lecture Notes in Computer Science, vol. 4191, pp. 718–725. Springer, Heidelberg (2006)

    Google Scholar 

  99. Roose, L., Loeckx, D., Mollemans, W., Maes, F., Suetens, P.: Adaptive boundary conditions for physically based follow-up breast mr image registration. In: Metaxas, D., Axel, L., Fichtinger, G., Szkely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2008, Lecture Notes in Computer Science, vol. 5242, pp. 839–846. Springer, Heidelberg (2008)

    Google Scholar 

  100. 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), 712–721 (1999)

    Article  Google Scholar 

  101. Ruiter, N.V.: Registration of X-ray mammograms and MR-volumes of the female breast based on simulated mammographic deformation PhD thesis, der Universitat Mannheim (2003)

    Google Scholar 

  102. Ruiter, N.V., Stotzka, R., Muller, T.O., Gemmeke, H., Reichenbach, J.R., Kaiser, W.A.: Model-based registration of X-ray mammograms and MR images of the female breast. In: IEEE Transactions on Nuclear Science, Institute of Electrical and Electronics Engineers, New York, NY, ETATS-UNIS, vol. 53, p. 8, anglais (2006)

    Google Scholar 

  103. Samani, A., Plewes, D.: A method to measure the hyperelastic parameters of ex vivo breast tissue samples. Phys. Med. Biol. 49(18), 4395 (2004)

    Article  Google Scholar 

  104. Samani, A., Plewes, D.: An inverse problem solution for measuring the elastic modulus of intact ex vivo breast tissue tumours. Phys. Med. Biol. 52(5), 1247 (2007)

    Article  Google Scholar 

  105. Samani, A., Bishop, J., Yaffe, M.J., Plewes, D.B.: Biomechanical 3-d finite element modeling of the human breast using mri data. IEEE Trans. Med. Imaging 20(4), 271–279 (2001)

    Article  Google Scholar 

  106. Samani, A., Bishop, J., Luginbuhl, C., Plewes, D.B.: Measuring the elastic modulus of ex vivo small tissue samples. Phys. Med. Biol. 48(14), 2183 (2003)

    Article  Google Scholar 

  107. Samani, A., Zubovits, J., Plewes, D.B.: Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples. Phys. Med. Biol. 52(6), 1565–1576 (2007)

    Article  Google Scholar 

  108. Sarkar, S., Zhang, Y., Qiu, Y., Goldgof, D., Li, L.: 3d finite element modeling of nonrigid breast deformation for feature registration in -ray and mr images. In: Applications of Computer Vision, 2007. WACV ’07. IEEE Workshop on, p. 38 (2007)

    Google Scholar 

  109. Schnabel, J.A., Tanner, C., Castellano-Smith, A.D., Degenhard, A., Leach, M.O., Hose, D.R., Hill, D.L.G., Hawkes, D.J.: Validation of nonrigid image registration using finite-element methods: application to breast mr images. IEEE Trans. Med. Imaging 22(2), 238–247 (2003)

    Article  Google Scholar 

  110. Shih, T.C., Chen, J.H., Liu, D., Nie, K., Sun, L., Lin, M., Chang, D., Nalcioglu, O., Su, M.Y.: Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images. Phys. Med. Biol. 55(14), 4153–4168 (2010)

    Article  Google Scholar 

  111. Sinkus, R., Weiss, S., Wigger, E., Lorenzen, J., Dargatz, M., Kuhl, C.: Nonlinear elastic tissue properties of the breast measured by mr-elastography: initial in vitro and In vivo results. In: Proceedings ISMRM 10th Annual Meeting, p. 33 (2002)

    Google Scholar 

  112. Sivaramakrishna, R.: 3d breast image registration–a review. Technol. Cancer Res. Treat. 4(1), 39–48 (2005)

    Google Scholar 

  113. Spear, S.L., Spittler, C.J.: Breast reconstruction with implants and expanders. Plast. Reconstr. Surg. 107(1), 177–87 (2001)

    Article  Google Scholar 

  114. Stein, K., Tezduyar, T.E., Benney, R.: Automatic mesh update with the solid-extension mesh moving technique. Comput. Methods Appl. Mech. Eng. 193, 2019–2032 (2004)

    Article  MATH  Google Scholar 

  115. Tanner, C., Degenhard, A., Schnabel, J., Smith, A., Hayes, C., Sonoda, L., Leach, M., Hose, D., Hill, D., Hawkes, D.: A method for the comparison of biomechanical breast models. In: Mathematical Methods in Biomedical Image Analysis, 2001. MMBIA 2001. IEEE Workshop on, pp. 11–18 (2001)

    Google Scholar 

  116. Tanner, C., Schnabel, J., Degenhard, A., Castellano-Smith, A., Hayes, C., Leach, M., Hose, D., Hill, D., Hawkes, D.: Validation of volume-preserving non-rigid registration: application to contrast-enhanced mr-mammography. In: Dohi, T., Kikinis, R. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI, Lecture Notes in Computer Science, vol. 2488, pp. 307–314. Springer, Heidelberg (2002)

    Google Scholar 

  117. Tanner, C., Carter, T., Hawkes, D.J.: 3d rezoning for finite element modelling of large breast deformations. In: European Modelling Symposium, pp. 51–53 (2006a)

    Google Scholar 

  118. Tanner, C., Schnabel, J.A., Hill, D.L.G., Hawkes, D.J., Leach, M.O., Hose, D.R.: Factors influencing the accuracy of biomechanical breast models. Med. Phys. 33(6), 1758–1769 (2006b)

    Article  Google Scholar 

  119. Tanner, C., Hipwell, J., Hawkes, D.: Statistical deformation models of breast compressions from biomechanical simulations. In: Krupinski, E. (ed.) Digital Mammography, Lecture Notes in Computer Science, vol. 5116, pp. 426–432. Springer, Heidelberg (2008)

    Google Scholar 

  120. Tanner C., White M., Guarino S., Hall-Craggs M.A., Douek M., Hawkes D.J. (2009) Anisotropic behaviour of breast tissue for large compressions. In: ISBI’09: Proceedings of the 6th IEEE international conference on Symposium on Biomedical Imaging. IEEE Press, Piscataway, NJ, pp. 1223–1226

    Google Scholar 

  121. Tanner, C., Hipwell, J.H., Hawkes, D.J.: Digital Mammography, Breast Shapes on Real and Simulated Mammograms, Chap Breast Shapes on Real and Simulated Mammograms, Lecture Notes in Computer Science, pp. 540–547. Springer, Heidelberg (2010)

    Google Scholar 

  122. Tanner, C., White, M., Guarino, S., Hall-Craggs, M.A., Douek, M., Hawkes, D.J.: Large breast compressions: observations and evaluation of simulations. Med. Phys. 38(2), 682–690 (2011)

    Article  Google Scholar 

  123. Unnikrishnan, G.U., Unnikrishnan, V.U., Reddy, J.N., Lim, C.T.: Review on the constitutive models of tumor tissue for computational analysis. Appl. Mech. Rev. 63(4), 040801 (2010)

    Article  Google Scholar 

  124. Veronesi, U., Boyle, P., Goldhirsch, A., Orecchia, R., Viale, G.: Breast cancer. The Lancet 365(9472), 1727–1741 (2005)

    Article  Google Scholar 

  125. Wellman, P.S., Howe, R.D., Dalton, E., Kern, K.A.: Breast Tissue Stiffness in Compression is Correlated to Histological Diagnosis, Technical Report. Harvard University, Cambridge (1999)

    Google Scholar 

  126. Wessel, C., Schnabel, J., Brady, S.: Towards more realistic biomechanical modelling of tumours under mammographic compressions. In: Mart, J., Oliver, A., Freixenet, J., Mart, R. (eds.) Digital Mammography, Lecture Notes in Computer Science, vol. 6136, pp. 481–489. Springer, Heidelberg (2010)

    Google Scholar 

  127. Whiteley, J.P., Gavaghan, D.J., Chapman, S.J., Brady, J.M.: Non-linear modelling of breast tissue. Math. Med. Biol. 24(3), 327–345 (2007)

    Article  MATH  Google Scholar 

  128. Wilhelmi, B.J., Blackwell, S.J., Phillips, L.G.: Langer’s lines: to use or not to use. Plast. Reconstr. Surg. 104(1), 208–214 (1999)

    Article  Google Scholar 

  129. Willson, S.A., Adam, E.J., Tucker, A.K.: Patterns of breast skin thickness in normal mammograms. Clin. Radiol. 33(6), 691–693 (1982)

    Article  Google Scholar 

  130. Wirth, M.: A nonrigid approach to medical image registration: matching images of the breast. PhD thesis, RMIT University (2000)

    Google Scholar 

  131. Yeh, W.C., Li, P.C., Jeng, Y.M., Hsu, H.C., Kuo, P.L., Li, M.L., Yang, P.M., Lee, P.H.: Elastic modulus measurements of human liver and correlation with pathology. Ultrasound Med. Biol. 28(4), 467–474 (2002)

    Article  Google Scholar 

  132. Yin, H.M., Sun, L.Z., Wang, G., Yamada, T., Wang, J., Vannier, M.W.: Imageparser: a tool for finite element generation from three-dimensional medical images. Biomed. Eng. Online 3, 31 (2004)

    Article  Google Scholar 

  133. Zienkiewicz, O., Taylor, R.: The Finite Element Method: The Basis. vol. 1, 5th edn. Butterworth-Heinemann, London (2000)

    Google Scholar 

  134. Zienkiewicz, O., Taylor, R.: The finite element method for solid and structural mechanics. 6th edn. Elsevier Butterworth-Heinemann, London (2005)

    MATH  Google Scholar 

  135. Zyganitidis, C., Bliznakova, K., Pallikarakis, N.: A novel simulation algorithm for soft tissue compression. Med. Biol. Eng. Comput. 45, 661–669 (2007)

    Article  Google Scholar 

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Acknowledgements

The financial support provided by the Foundation for Research, Science and Technology in New Zealand is gratefully acknowledged. We also thank Angela Lee, Richard Boyes, Hayley Reynolds, Jessica Jor and Tim Wu for their valuable contributions. M.P. Nash and P.M.F. Nielsen are supported by James Cook Fellowships administered by the Royal Society of New Zealand on behalf of the New Zealand Government.

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Babarenda Gamage, T.P., Rajagopal, V., Nielsen, P.M.F., Nash, M.P. (2011). Patient-Specific Modeling of Breast Biomechanics with Applications to Breast Cancer Detection and Treatment. In: Gefen, A. (eds) Patient-Specific Modeling in Tomorrow's Medicine. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 09. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8415_2011_92

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