Breast image registration and deformation modeling

Critical Reviews in Biomedical Engineering

Volumn 40, Issue 3, 2012, Pages 235-258

  Boehler, Tobias ^a   Zoehrer, Fabian ^a   Harz, Markus ^a   Hahn, Horst Karl ^a  

^a FRAUNHOFER MEVIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BREAST DISEASE; BREAST IMAGES; CLINICAL DATA; DEFORMATION MODELING; HIGH VARIABILITY; IMAGE-BASED; INDISPENSABLE TOOLS; MALIGNANT LESION; MULTI-MODAL; TISSUE DEFORMATIONS;

BIOPHYSICS;

IMAGE REGISTRATION;

GADOLINIUM;

BREAST; BREATHING MECHANICS; CONTRAST ENHANCEMENT; DIAGNOSTIC IMAGING; DIFFUSION WEIGHTED IMAGING; ECHOMAMMOGRAPHY; HEART CYCLE; HUMAN; IMAGE ANALYSIS; MAMMOGRAPHY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATIENT POSITIONING; PECTORAL MUSCLE; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; REVIEW; SCREENING TEST; AUDIOVISUAL EQUIPMENT; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; FEMALE; IMAGE PROCESSING; METHODOLOGY; MOTION; PATHOLOGY;

BREAST; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MAMMOGRAPHY; MODELS, ANATOMIC; MOTION; ULTRASONOGRAPHY, MAMMARY;

EID: 84865148649     PISSN: 0278940X     EISSN: None     Source Type: Journal    
DOI: 10.1615/CritRevBiomedEng.v40.i3.60     Document Type: Review

References (217)

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010; vol. 127 (12), pp. 2893-2917.
2. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA: A cancer journal for clinicians. 2010;60(5):277-300.
3. Kriege M, Brekelmans CTM, Boetes C, Besnard PE, Zonderland HM, Obdeijn IM, Manoliu RA, Kok T, Peterse H, Tilanus-Linthorst MMA, Muller SH, Meijer S, Oosterwijk JC, Beex LVAM, Tollenaar RAEM, de Koning HJ, Rutgers EJT, and Klijn JGM. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. New Engl J Med. 2004;351(5):427-437.
4. Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S, Smith RA, Warner E, Yaffe M, Andrews KS, Russell CAAmerican Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA: A cancer journal for clinicians. 2007;57(2):75-89.
5. Suri JS, Rangayyan RM. Recent advances in breast imaging, mammography, and computer-aided diagnosis of breast cancer. Bellingham, WA: SPIE Press; 2006.
6. Berg, W.A. and Birdwell, R.L. and Gombos, E.C. and Wang, S.C. and Parkinson, B.T. and Raza, S. and Green, GE and Kennedy, A. and Kettler, MD. Diagnostic imaging: breast. Amirsys, 2006.
7. Guo Y, Suri J, Sivaramakrishna R. Image registration for breast imaging: a review. Proceedings of 27th Annual International Conference of the Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE; 2005. p. 3379-82.
8. Sivaramakrishna R. 3D breast image registration-a review. Technol Cancer Res & Treatment. 2005;4(1):39-48.
9. Guo Y, Sivaramakrishna R, Lu CC, Suri J, Laxminarayan S. Breast image registration techniques: a survey. Med and Biol Eng Comput. 2006;44:15-26.
10. Tabár, L. and Vitak, B. and Chen, H.H. and Duffy, S.W. and Yen, M.F. and Chiang, C.F. and Krusemo, U.B. and Tot, T. and Smith, R.A. The Swedish two-county trial twenty years later: updated mortality results and new insights from long-term follow-up. Radiol Clin North Am. 2000;38(4):625-51.
11. Kuhl, C.K. and Schrading, S. and Leutner, C.C. and Morakkabati-Spitz, N. and Wardelmann, E. and Fimmers, R. and Kuhn, W. and Schild, H.H. Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol. 2005;23(33):8469-76.
12. Chen Z, Ning R. Why should breast tumour detection go three dimensional? Phys Med and Biol. 2003;48(14):2217-28.
13. Gram IT, Funkhouser E, Tabár L. The Tabar classification of mammographic parenchymal patterns. Eur J Radiol. 1997;24(2):131-6.
14. Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002;225(1):165-75.
15. Enriquez L, Listinsky J. Role of MRI in breast cancer management. Cleveland Clin J Med. 2009;76(9):525-32.
16. Chou YH, Tiu CM, Chen J, Chang RF. Automated full-field breast ultrasonography: the past and the present. J Med Ultrasound. 2007;15(1):31-44.
17. Leproux, A. and Van Beek, M. and De Vries, U. and Wasser, M. and Bakker, L. and Cuisenaire, O. and Van der Mark, M. and Entrekin, R. Automated 3D whole-breast ultrasound imaging: results of a clinical pilot study. Proc. of SPIE Medical Imaging. Vol. 7629, Bellingham, WA: SPIE; 2010. p. 762902.
18. J.A. Shipley and F.A. Duck and D.A. Goddard and M.R. Hillman and M. Halliwell and M.G. Jones and B.T. Thomas. Automated quantitative volumetric breast ultrasound data-acquisition system. Ultrasound MedBiol. 2005;31(7):905-17.
19. Cho KR, Seo BK, Lee JY, Pisano ED, Je BK, Lee JY, et al. A comparative study of 2D and 3D ultrasonography for evaluation of solid breast masses. Eur J Radiol. 2005;54(3):365-70.
20. Watermann, D.O. and Foeldi, M. and Hanjalic-Beck, A. and Hasenburg, A. and Lughausen, A. and Prompeler, H. and Gitsch, G. and Stickeler, E. Three-dimensional ultrasound for the assessment of breast lesions. Ultrasound Obstet Gynecol. 2005;25:592-8.
21. Sahiner, B. and Chan, H.P. and Roubidoux, M.A. and Hadjiiski, L.M. and Helvie, M.A. and Paramagul, C. and Bailey, J. and Nees, A.V. and Blane, C. Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. Radiology. 2007;242(3):716-24.
22. Berg WA. Rationale for a trial of screening breast ultrasound: American College of Radiology Imaging Network (ACRIN) 6666. Am J Roentgenol. 2003;180(5):1225-8.
23. Weismann C, Datz L. Diagnostic algorithm: How to make use of new 2D, 3D and 4D ultrasound technologies in breast imaging. Eur J Radiol. 2007;64(2):250-7.
24. Kaiser W, Zeitler E. MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Radiology. 1989;170(3):681-6.
25. Kuhl, C.K. and Mielcareck, P. and Klaschik, S. and Leutner, C. and Wardelmann, E. and Gieseke, J. and Schild, H.H. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211(1):101-10.
26. Sinha, S.P. and Roubidoux, M.A. and Helvie, M.A. and Nees, A.V. and Goodsitt, M.M. and LeCarpentier, G.L. and Fowlkes, J.B. and Chalek, C.L. and Carson, P.L. Multi-modality 3D breast imaging with x-ray tomosynthesis and automated ultrasound. Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE; 2007. p. 1335-8.
27. Tafra L. Positron emission mammography: a new breast imaging device. J Surg Oncol. 2008;97(5):372-3.
28. Fischer B, Modersitzki J. Ill-posed medicine-an introduction to image registration. Inverse Prob. 2008;24:1-16.
29. Maintz JBA, Viergever MA. An overview of medical image registration methods. Med Image Anal. 1998;2:1-36.
30. Zitova B, Flusser J. Image registration methods: a survey. Image Vi Comput. 2003;21:997-1000.
31. Modersitzki J. FAIR: flexible algorithms for image registration. Society for I A Math; 2009.
32. Hajnal JV, Hawkes DJ, Hill DLG. Medical image registration. Boca Raton: CRC Press; 2001.
33. Modersitzki J. Numerical methods for image registration. London: Oxford University Press; 2004.
34. Rueckert D, Sonoda L.I., Denton E.R., Rankin S., Hayes C, Leach M.O., Hill D.L., Hawkes D.J. Comparison and evaluation of rigid and nonrigid registration of breast MR images. Proceedings of SPIE Medical Imaging. Vol. 3661. Bellingham, WA: SPIE; 1999. p. 78-88.
35. Zikic D, Kamen A, Navab N. Unifying Characterization of Deformable Registration Methods Based on the Inherent Parametrization. Proceedings of Workshop on Biomedical Image Registration. New York: Springer; 2010. p. 161-72.
36. Rohr K, Stiehl H, Sprengel R, Buzug T, Weese J, Kuhn M. Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans on Med Imaging. 2001;20(6):526-34.
37. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712-21.
38. Rohde GK, Aldroubi A, Dawant BM. The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans Med Imaging. 2003;22(11):1470-9.
39. Christensen GE, Rabbitt R, Miller M. Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing. 1996;5(10):1435-1447.
40. Fischer B, Modersitzki J. Fast diffusion registration. AMS Special Session on Interaction of Inverse Problems and Image Analysis. 2002;3:117-28.
41. Fischer B, Modersitzki J. Curvature based image registration. J Math Imaging Vis. 2003;18(1):81-5.
42. Gramkow C, Bro-Nielsen M. Comparison of three filters in the solution of the Navier-Stokes Equation in registration. Proceedings of the Scandinavian Conference on Image Analysis. Lappeenranta: Pattern Recognition Society of Finland;1997; p. 795-802.
43. Thirion JP. Image matching as a diffusion process: an analogy with Maxwell's demons. Med Image Anal. 1998;2(3):243-60.
44. Pennec X, Cachier P, Ayache N. Understanding the demon's algorithm: 3D non-rigid registration by gradient descent. Proceedings of MICCAI. Cambridge, UK: Springer; 1999. p. 597-605.
45. Vercauteren T, Pennec X, Perchant A, Ayache N. Non-parametric diffeomorphic image registration with the demons algorithm. Proceedings of MICCAI. Vol. 4792. New York: Springer; 2007. p. 319-26.
46. Cahill ND, Noble JA, Hawkes DJ. Demons algorithms for fluid and curvature registration. Proceedings of IEEE International Symposium on Biomedical Imaging. Piscataway, NJ: IEEE; 2009. p. 730-3.
47. Nealen A, Müller M, Keiser R, Boxerman E, Carlson M; Wiley Online Library. Physically based deformable models in computer graphics. Comput Graphics Forum. 2006;25(4):809-36.
48. Roose L, De Maerteleire W, Mollemans W, Suetens P. Validation of different soft tissue simulation methods for breast augmentation. Proceedigns of Computer Assisted Radiology and Surgery. Vol. 1281. New York: Elsevier; 2005. p. 485-90.
49. de Oliveira ACMTG, dos Santos Nunes FL. Building a open source framework for virtual medical training. J Digital Imaging. 2009;23(6):706-20.
50. Tanner, C and Schnabel, JA and Castellano-Smith, AD and Degenhard, A and Leach, MO and Hose, DR and Hill, DL and Hawkes, DJ. Validation of nonrigid image registration using finite-element methods: application to breast MR images. IEEE Trans Med Imaging. 2003;22(2):238-47.
51. Christine Tanner and Julia A. Schnabel and Derek L. G. Hill and David J. Hawkes and Andreas Degenhard and Martin O. Leach and D. Rodney Hose and Margaret A. Hall-Craggs and Sasha I. Usiskin. Quantitative evaluation of free-form deformation registration for dynamic contrast-enhanced MR mammography. Med Phy. 2007;34(4):1221-33.
52. Boehler T, Peitgen HO. Evaluation of image registration using a mass-spring model of the breast. Proceedings of World Congress on Medical Physics and Biomedical Engineering. New York: Springer; 2009. p. 2201-4.
53. Georgii J, Westermann R. A multigrid framework for real-time simulation of deformable bodies. Comput Graphics. 2006;30:408-15.
54. Han L, Hipwell J, Taylor Z, Tanner C, Ourselin S, Hawkes D. fast deformation simulation of breasts using GPU-based dynamic explicit finite element method. Proceedings of International Workshop on Digital Mammography. Marti J, Oliver A, Freixenet J, Marti R, editors vol. 6136. New York: Springer; 2010. p. 728-35.
55. Rajagopal V, Nielsen PMF, Nash MP. Modeling breast biomechanics for multi-modal image analysis-successes and challenges. Sys Biol Med. 2010;2(3):293-304.
56. Harz MT, Georgii J, Schilling K, Hahn HK. Towards navigated breast surgery using efficient breast deformation simulation. Proceedigns of MICCAI Workshop on Breast Image Analysis. Toronto: Department of Computer Science, University of Copenhagen, DK; 137-144.
57. Lee A, Schnabel J, Rajagopal V, Nielsen P, Nash M. Breast image registration by combining finite elements and free-form deformations. Proceedings of International Workshop on Digital Mammography. New York: Springer; 2010. p. 736-43.
58. Roche A, Malandain G, Pennec X, Ayache N. The correlation ratio as a new similarity measure for multimodal image registration. Proceedings of MICCAI.; 1998. p. 1115-24.
59. Netsch T, Roesch P, van Muiswinkel A, Weese J. Towards real-time multi-modality 3-D medical image registration. Proceedings of ICCV 2001; Vol. 1: Medical Imaging. Boston, USA: Springer; 2001. p. 718-25.
60. Viola P, Wells III WM. Alignment by maximization of mutual information. Int J Comput Vis. 1997;24(2):137-54.
61. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging. 1997;16(2):187-98.
62. Studholme, C. and Hill, DLG and Hawkes, DJ. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 1999;32(1):71-86.
63. Haber E, Modersitzki J. Intensity gradient based registration and fusion of multi-modal images. Proceedings of MICCAI, Vol. 4191. New York: Springer; 2006. p. 726-33.
64. Woods RP, Cherry SR, Mazziotta JC. Rapid automated algorithm for aligning and reslicing PET images. J Comput Assist Tomog. 1992;16(4):620-633.
65. Woods RP, Mazziotta JC, Cherry SR. MRI-PET registration with automated algorithm. J Comput Assist Tomog. 1993;17(4):536-546.
66. Modersitzki J, Wirtz S. Combining homogenization and registration. Proceedings of Workshop on Biomedical Image Registration. New York: Springer; 2006. p. 257-63.
67. Rohlfing T, Maurer CR, Bluemke DA, Jacobs MA. Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans Med Imaging. 2003;22:730-41.
68. Nocedal J, Wright SJ. Numerical optimization. Berlin: Springer-Verlag; 1999.
69. Maes F, Vandermeulen D, Suetens P. Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med Image Anal. 1999;3(4):373-86.
70. Kabus S, Netsch T, Fischer B, Modersitzki J. B-spline registration of 3D images with Levenberg-Marquardt optimization. Proc SPIE. 2004; 5370: p. 304-13.
71. Dennis JE, Schnabel RB. Numerical methods for unconstrained optimization and nonlinear equations. Philadelphia, USA: Society for Industrial and Applied Mathematics; 1996.
72. Klein S, Staring M, Pluim JPW. Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Trans on Image Process. 2007;16(12):2879-90.
73. Harvey JA, Hendrick RE, Coll JM, Nicholson BT, Burkholder BT, Cohen MA. Breast MR imaging artifacts: how to recognize and fix them. Radiographics. 2007;27(Suppl 1):S131-45.
74. Yam M, Brady M, Highnam R, Behrenbruch C, English R, Kita Y. Three-dimensional reconstruction of microcalcification clusters from two mammographic views. IEEE Trans Med Imaging. 2001;20(6):479-89.
75. Huo Z, Giger ML, Vyborny CJ. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis. IEEE Trans Med Imaging. 2001;20(12):1285-92.
76. Qiu Y, Goldgof D, Li L, Sarkar S, Zhang Y, Anton S. Correspondence recovery in 2-view mammography. Proceedings of IEEE International Symposium on Biomedical Imaging. Vol. 1. 2004. p. 197-200.
77. Paquerault S, Petrick N, Chan HP, Sahiner B, Helvie MA. Improvement of computerized mass detection on mammograms: Fusion of two-view information. Med Phys. 2002;29:238-47.
78. Tiedeu A, Daul C, Graebling P, Wolf D. Correspondences between microcalcification projections on two mammographic views acquired with digital systems. Comput Med Imaging Graphics. 2005;29(7):543-53.
79. Sahiner, B. and Chan, H.P. and Hadjiiski, L.M. and Helvie, M.A. and Paramagul, C. and Ge, J. and Wei, J. and Zhou, C. Joint two-view information for computerized detection of microcalcifications on mammograms. Med Phys. 2006;33:2574-85.
80. van Engeland S, Karssemeijer N. Combining two mammographic projections in a computer aided mass detection method. Med Phys. 2007;34:898-905.
81. Wei, J. and Chan, H.P. and Sahiner, B. and Zhou, C. and Hadjiiski, L.M. and Roubidoux, M.A. and Helvie, M.A. Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis. Med Phys. 2009;36:4451-60.
82. Velikova M, Samulski M, Lucas PJF, Karssemeijer N. Improved mammographic CAD performance using multi-view information: a Bayesian network framework. Phys Med and Biol. 2009;54(5):1131-47.
83. Pu J, Zheng B, Leader JK, Gur D. An ellipse-fitting based method for efficient registration of breast masses on two mammographic views. Med Phys. 2008;35:487-94.
84. van Engeland S, Timp S, Karssemeijer N. Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views. Med Phys. 2006;33:3203-3212.
85. Zheng B, Tan J, Ganott MA, Chough DM, Gur D. Matching breast masses depicted on different views: a comparison of three methods. Acad Radiol. 2009;16(11):1338-47.
86. Roelofs AA, Karssemeijer N, Wedekind N, Beck C, van Woudenberg S, Snoeren PR, Hendriks JH, Rosselli del Turco M, Bjurstam N, Junkermann H, Beijerinck D, Séradour B, Evertsz CJ. Importance of comparison of current and prior mammograms in breast cancer screening. Radiology. 2007;242(1):70-7.
87. Taylor-Phillips S, Wallis M, Gale A. Should previous mammograms be digitised in the transition to digital mammography? Eur Radiol. 2009;19(8):1890-6.
88. Sallam MY, Bowyer KW. Registration and difference analysis of corresponding mammogram images. Med Image Anal. 1999;3(2):103-18.
89. Marias K, Behrenbruch C, Parbhoo S, Seifalian A, Brady M. A registration framework for the comparison of mammogram sequences. IEEE Trans Med Imaging. 2005;24(6):782-90.
90. Sanjay-Gopal S, Chan HP, Wilson T, Helvie M, Petrick N, Sahiner B. A regional registration technique for automated interval change analysis of breast lesions on mammograms. Med Phys. 1999;26:2669-79.
91. Wirth MA, Choi C, Jennings A. A nonrigid-body approach to matching mammograms. Proceedings of Seventh International Conference on Image Processing and Its Applications. Vol. 2. Manchester, UK: IEEE Press 1999. p. 484-8.
92. Wirth MA, Narhan J, Gray D. Nonrigid mammogram registration using mutual information. Proc SPIE. 2002;4684:562-73.
93. Wirth MA, Choi C. Mammogram registration using the cauchy-navier spline. Proc SPIE. 2001; 4322:1654-65.
94. Qiu Y, Sun X, Manohar V, Goldgof D. Towards registration of temporal mammograms by finite element simulation of MR breast volumes. Proc. SPIE. 2008; 6918:69182F.
95. van Engeland S, Snoeren P, Hendriks J, Karssemeijer N. A comparison of methods for mammogram registration. IEEE Trans Med Imaging. 2003;22(11):1436-44.
96. Temmermans F, Deklerck R, Suliga M, Breucq C, Behiels G, Dewaele P. Intelligent synchronized magnifying glasses for assisting reading of temporal mammograms. Proceedings of 4th European Conference of the International Federation for Medical and Biological Engineering. New York: Springer; 2009. p. 1034-7.
97. Snoeren PR, Karssemeijer N. Gray-scale and geometric registration of full-field digital and film-screen mammograms. Med Image Anal. 2007;11(2):146-56.
98. Diez, Y. and Oliver, A. and Llado, X. and Marti, R. Comparison of registration methods using mamographic images. Proceedings of 17th IEEE International Conference on Image Processing. Piscataway, NJ: IEEE; 2010. p. 4421-4.
99. Brandt S, Karemore G, Karssemeijer N, Nielsen M. An anatomically oriented breast coordinate system for mammogram analysis. IEEE Trans Med Imaging. 2011;30(10):1841-51.
100. Timp S, van Engeland S, Karssemeijer N. A regional registration method to find corresponding mass lesions in temporal mammogram pairs. Med Phys. 2005;32:2629-38.
101. Timp S, Varela C, Karssemeijer N. Computer-aided diagnosis with temporal analysis to improve radiologists interpretation of mammographic mass lesions. IEEE Trans Info Technol Biomed. 2010;14(3):803-8.
102. Timp S, Varela C, Karssemeijer N. temporal change analysis for characterization of mass lesions in mammography. IEEE Trans Med Imaging. 2007;26(7):945-53.
103. Marias K, Behrenbruch C, Highnam R, Parbhoo S, Seifalian A, Brady M. A mammographic image analysis method to detect and measure changes in breast density. Eur J Radiol. 2004;52(3):276-82.
104. Ma F, Bajger M, Bottema MJ. A graph matching based automatic regional registration method for sequential mammogram analysis. Proc SPIE 2008; 6915:69151Z.
105. Bajger M, Ma F, Bottema MJ. Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms. Proceedings of Digital Image Computing: Techniques and Applications, 2009. Melbourne, AUS: IEEE Press; 2009. p. 400-7.
106. Hadjiiski L, Chan HP, Sahiner B, Petrick N, Helvie MA. Automated registration of breast lesions in temporal pairs of mammograms for interval change analysisLocal affine transformation for improved localization. Med Phys. 2001;28:1070-9.
107. Filev P, Hadjiiski L, Sahiner B, Chan HP, Helvie MA. Comparison of similarity measures for the task of template matching of masses on serial mammograms. Med Phys. 2005;32:515-29.
108. Lehman CD, Gatsonis C, Kuhl CK, Hendrick RE, Pisano ED, Hanna L, Peacock S, Smazal SF, Maki DD, Julian TB, DePeri ER, Bluemke DA, Schnall MD. MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. New Engl J Med. 2007;356(13):1295-1303.
109. Lu W, Schaapveld M, Jansen L, Bagherzadegan E, Sahinovic MM, Baas PC, Hanssen LM, van der Mijle HC, Brandenburg JD, Wiggers T, De Bock GH. The value of surveillance mammography of the contralateral breast in patients with a history of breast cancer. Eur J Cancer. 2009;45(17):3000-7.
110. Richard FJP, Cohen LD. A new image registration technique with free boundary constraints: application to mammography. Comput Vis Image Understand. 2003;89(2-3):166-96.
111. Reig-Bolano R, Baradad V, Marti-Puig P. A contour based approach for bilateral mammogram registration using discrete wavelet analysis. Proceedings of International Symposium on Distributed Computing and Artificial Intelligence. New York: Springer; 2009. p. 347-56.
112. Yang, Z. and Sinha, S.P. and Booi, R.C. and Roubidoux, M.A. and Ma, B. and Fowlkes, J.B. and LeCarpentier, G.L. and Carson, P.L. Breast ultrasound image improvement by pixel compounding of compression sequence. IEEE Trans on Ultrason Ferroelect and Freq Control. 2009;56(3):465-73.
113. Xiao G, Brady JM, Noble JA, Burcher M, English R. Nonrigid registration of 3-D free-hand ultrasound images of the breast. IEEE Trans Med Imaging. 2002;21(4):405-12.
114. Krucker JF, LeCarpentier GL, Fowlkes JB, Carson PL. Rapid elastic image registration for 3-D ultrasound. IEEE Trans on Med Imaging. 2002;21(11):1384-94.
115. Moskalik A, Carson P, Meyer C, Fowlkes J, Rubin J, Roubidoux M. Registration of three-dimensional compound ultrasound scans of the breast for refraction and motion correction. Ultrasound Med Biol. 1995;21(6):769-78.
116. Chen CJ, Chang RF, Chen DR. Automatic tumor diagnosis for breast ultrasound using 3D sub-volume registration. Proceedings of Ninth IEEE International Conference on Bioinformatics and Bioengineering. Piscataway, NJ: IEEE; 2009. p. 61-8.
117. Chang, R.F. and Chang-Chien, K.C. and Takada, E. and Huang, C.S. and Chou, Y.H. and Kuo, C.M. and Chen, J.H. Rapid image stitching and computer-aided detection for multipass automated breast ultrasound. Med Phys. 2010;37:2063-74.
118. Treece G, Prager R, Gee A, Berman L. Correction of probe pressure artifacts in freehand 3D ultrasound. Med Image Anal. 2002;6(3):199-214.
119. Gee A, Prager R, Treece G, Cash C, Berman L. Processing and visualizing three-dimensional ultrasound data. Br J Radiol. 2004;77(2):S186-93.
120. Lin CH, Sun YN, Lin CJ. A motion compounding technique for speckle reduction in ultrasound images. J Dig Imaging. 2010;23(3):246-57.
121. Boehler T, Peitgen HO. Reducing motion artifacts in 3-D breast ultrasound using non-linear registration. Proceedings of MICCAI. vol. 5242. New York: Springer; 2008. p. 998-1005.
122. Dave JK, Forsberg F. Novel automated motion compensation technique for producing cumulative maximum intensity sub-harmonic images. Ultrasound Med Biol. 2009;35(9):1555-63.
123. Chen CJ, Chang RF, Moon WK, Chen DR, Wu HK. 2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration. Ultrasound Med Biol. 2006;32(6):837-46.
124. Tan T, Huisman H, Platel B, Grivegnee A, Mus R, Karssemeijer N. Classification of breast lesions in automated 3D breast ultrasound. Proc SPIE 2011; 7963:79630X.
125. Narayanasamy G, LeCarpentier GL, Roubidoux M, Fowlkes JB, Schott AF, Carson PL. Spatial registration of temporally separated whole breast 3D ultrasound images. Med Phys. 2009;36:4288-4300.
126. Narayanasamy, G. and LeCarpentier, G.L. and Zabuawala, S. and Fowlkes, J.B. and Roubidoux, M. and Sinha, S. and Carson, P.L. Non-rigid registration of three-dimensional (3D) grayscale and Doppler ultrasound breast images. Proceedings of 29th Annual International Conference of IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE; 2007. p. 91-4.
127. Lu X, Zhang S, Yang W, Chen Y. SIFT and shape information incorporated into fluid model for non-rigid registration of ultrasound images. Comput Methods Prog Biomed. 2010;100(2):123-31.
128. Fenster A, Surry KJM, Mills G, Downey D. 3D ultrasound guided breast biopsy system. Ultrasonics. 2004;42(1-9):769-74.
129. Lucht R, Knopp MV, Brix G. Elastic matching of dynamic MR mammographic images. Magn Reson Med. 2000;43(1):9-16.
130. Kumar, R. and Asmuth, J.C. and Hanna, K. and Bergen, J. and Hulka, C. and Kopans, D.B. and Weisskoff, R. and Moore, R. Application of 3D registration for detecting lesions in magnetic resonance breast scans. Proc SPIE. 1996;2710:646-56.
131. Hayton PM, Brady M, Smith SM, Moore N. A non-rigid registration algorithm for dynamic breast MR images. Artif Intel. 1999;114:125-56.
132. Tanner, C. and Schnabel, J. and Chung, D. and Clarkson, M. and Rueckert, D. and Hill, D. and Hawkes, D. Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images. Proceedings of MICCAI. Vol. 1935. New York: Springer; 2000. p. 291-300.
133. Rohlfing T, Maurer C, Bluemke DA, Jacobs MA. An alternating-constraints algorithm for volume-preserving non-rigid registration of contrast-enhanced MR breast images. Proceedings of Workshop on Biomedical Image Registration. New York: Springer; 2003. p. 291-300.
134. Crum W, Tanner C, Hawkes D. Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging. Phys Med Biol. 2005;50:5153-74.
135. Mainardi, L. and Passera, KM and Lucesoli, A. and Vergnaghi, D. and Trecate, G. and Setti, E. and Musumeci, R. and Cerutti, S. A nonrigid registration of MR breast images using complex-valued wavelet transform. J Dig Imaging. 2008;21(1):27-36.
136. Zheng Y, Yu J, Kambhamettu C, Englander S, Schnall MD, Shen D. De-enhancing the dynamic contrast-enhanced breast MRI for robust registration. Proceedings of MICCAI. Vol. 4791. Brisbane, AUS: Springer; 2007. p. 933-41.
137. Xiaohua C, Brady M, Lo JLC, Moore N. Simultaneous segmentation and registration of contrast-enhanced breast MRI. Information processing in medical imaging. New York: Springer; 2005. p. 126-137.
138. Gong YC, Brady M. Texture-based simultaneous registration and segmentation of breast DCE-MRI. Proceedings of International Workshop on Digital Mammography. Vol. 5116. New York: Springer; 2008. p. 174-180.
139. Herrmann KH, Wurdinger S, Fischer DR, Krumbein I, Schmitt M, Hermosillo G, Chaudhuri K, Krishnan A, Salganicoff M, Kaiser WA, Reichenbach JR. Application and assessment of a robust elastic motion correction algorithm to dynamic MRI. Eur Radiol. 2007;17(1):259-64.
140. Martel A, Froh M, Brock K, Plewes D, Barber D. Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast. Phys Med Biol. 2007;52:3803-16.
141. Modat, M. and Ridgway, G.R. and Taylor, Z.A. and Lehmann, M. and Barnes, J. and Hawkes, D.J. and Fox, N.C. and Ourselin, S. Fast free-form deformation using graphics processing units. Comput Methods Prog Biomed. 2010;98:278-84.
142. Boehler T, Schilling K, Bick U, Hahn HK. Deformable registration of differently-weighted breast magnetic resonance images. Proceedings of Bildverarbeitung fuer die Medizin. Luebeck Springer; 2011. p. 94-8.
143. Sickles E. Periodic mammographic follow-up of probably benign lesions: results in 3,184 consecutive cases. Radiology. 1991;179(2):463-68.
144. Varas X, Leborgne JH, Leborgne F, Mezzera J, Jaumandreu S, Leborgne F. Revisiting the mammographic follow-up of BI-RADS category 3 lesions. Am J Roentgenol. 2002;179(3):691-5.
145. Montgomery D, Krupa K, Cooke T. Alternative methods of follow up in breast cancer: a systematic review of the literature. Br J cancer. 2007;96(11):1625-32.
146. Roose L, Loeckx D, Mollemans W, Maes F, Suetens P. Adaptive boundary conditions for physically based follow-up breast MR image registration. Proceedings of MICCAI. New York, US: Springer; 2008. p. 839-46.
147. Boehler T, Bick U, Hahn HK. Assessment of image registration for follow-up MR mammography. Eur Radiol Supp. 2009;19(4):961-63.
148. Boehler T, Schilling K, Bick U, Hahn HK. Deformable image registration of follow-up breast magnetic resonance images. Proceedings of Workshop on Biomedical Image Registration. New York: Springer; 2010. p. 13-24.
149. Chittineni R, Su MY, Nalcioglu O. Breast delineation using active contours to facilitate coregistration of serial MRI studies for therapy response evaluation. IEEE International Conference on Image Processing. Vol. 6. Piscataway, NJ: IEEE; 2007. p. 261-4.
150. Li, X. and Dawant, B.M. and Welch, E.B. and Chakravarthy, A.B. and Freehardt, D. and Mayer, I. and Kelley, M. and Meszoely, I. and Gore, J.C. and Yankeelov, T.E. A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response. Magn Reson Imaging. 2009;27(9):1258-70.
151. Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging. 2010;31(3):562-70.
152. Ma, Bing and Meyer, Charles and Pickles, Martin and Chenevert, Thomas and Bland, Peyton and Galban, Craig and Rehemtulla, Alnawaz and Turnbull, Lindsay and Ross, Brian. Voxel-by-voxel functional diffusion mapping for early evaluation of breast cancer treatment. Information Processing in Medical Imaging. Vol. 5636. New York: Springer; 2009. p. 276-87.
153. Arlinghaus, Lori R. and Welch, E. Brian and Chakravarthy, A. Bapsi and Xu, Lei and Farley, Jaime S. and Abramson, Vandana G. and Grau, Ana M. and Kelley, Mark C. and Mayer, Ingrid A. and Means-Powell, Julie A. and Meszoely, Ingrid M. and Gore, John C. and Yankeelov, Thomas E. Motion correction in diffusion-weighted MRI of the breast at 3T. J Magn Reson Imaging. 2011;33(5):1063-70.
154. Yang, G. and Hipwell, J. and Clarkson, M. and Tanner, C. and Mertzanidou, T. and Gunn, S. and Ourselin, S. and Hawkes, D. and Arridge, S. Combined reconstruction and registration of digital breast tomosynthesis. Proceedings of International Workshop on Digital Mammography. New York: Springer; 2010. p. 760-68.
155. van Schie, G. and Tanner, C. and Snoeren, P. and Samulski, M. and Leifland, K. and Wallis, M.G. and Karssemeijer, N. Correlating locations in ipsilateral breast tomosynthesis views using an analytical hemispherical compression model. Phys Med Biol. 2011;56:4715-30.
156. Karssemeijer, N. and Huisman, H. and Hawkes, D. and Hipwell, J. and Boehler, T. and Lesniak, J. and Tanner, C. and Szekely, G. and Niessen, W. and Hahn, H. Integrating biological knowledge, novel imaging modalities, and modeling in breast cancer diagnosis. Proceedings of IEEE International Symposium on Biomedical Imaging. Piscataway, NJ: IEEE; 2009. p. 386-89.
157. Behrenbruch, C.P. and Marias, K. and Armitage, P.A. and Yam, M. and Moore, N. and English, R.E. and Clarke, J. and Brady, M. Fusion of contrast-enhanced breast MR and mammographic imaging data. Med Image Anal.
158. Ruiter N, Muller T, Stotzka R, Gemmeke H, Reichenbach J, Kaiser W. Registration of x-ray mammograms and MR-volumes of the female breast based on simulated mammographic deformation. Proceedings of International Workshop on Digital Mammography. Berlin: Mensch und Buch Verlag; 2004 p.1-10.
159. Ruiter NV, Stotzka R, Muller TO, Gemmeke H, Reichenbach JR, Kaiser WA. Model-based registration of x-ray mammograms and MR images of the female breast. IEEE Trans Nucl Sci. 2006;53(1):204-11.
160. Dietzel, M. and Hopp, T. and Ruiter, N. and Zoubi, R. and Runnebaum, I.B. and Kaiser, W.A. and Baltzer, P.A.T. Fusion of dynamic contrast-enhanced magnetic resonance mammography at 3.0 T with x-ray mammograms: pilot study evaluation using dedicated semi-automatic registration software. Eur J Radiol. 2011;97(2):98-102.
161. Hipwell JH, Tanner C, Crum WR, Schnabel JA, Hawkes DJ. A new validation method for x-ray mammogram registration algorithms using a projection model of breast x-ray compression. IEEE Trans Med Imaging. 2007;26(9):1190-1200.
162. Mertzanidou T, Hipwell JH, Tanner C, Hawkes DJ. An intensity-based approach to x-ray mammography: MRI registration. Proc SPIE 2010; 7623:76232Z.
163. Mertzanidou T, Hipwell J, Cardoso M, Tanner C, Ourselin S, Hawkes D. x-ray mammography-MRI registration using a volume-preserving affine transformation and an EM-MRF for breast tissue classification. Proceedings of International Workshop on Digital Mammography. New York: Springer; 2010. p. 23-30.
164. Martí R, Zwiggelaar R, Rubin CME, Denton ERE. Two-dimensional-three-dimensional correspondence in mammography. Cybern and Syst. 2004;35(1):85-105.
165. Marti R, Zwiggelaar R, Rubin C. Automatic registration of mammograms based on linear structures. Proceedings of Information Processing in Medical Imaging. Vol. 2082. New York: Springer; 2001. p. 162-8.
166. Rajagopal V, Nash M, Highnam R, Nielsen P. The breast biomechanics reference state for multi-modal image analysis. Proceedings of International Workshop on Digital Mammography. Vol. 5116. Krupinski E, editor. New York: Springer; 2008. p. 385-92.
167. Chung J, Rajagopal V, Nielsen PMF, Nash M. A biomechanical model of mammographic compressions. Biomech Model Mechanobiol. 2008;7(1):43-52.
168. Rajagopal V, Chung JH, Highnam RP, Warren R, Nielsen PMF, Nash MP. Mapping microcalcifications between 2D mammograms and 3D MRI using a biomechanical model of the breast. Comput Biomech Med. 2010;Proceedings Workshop Computational Biomechanics for Medicine 2010, Beijing, China, Springer. 17-28.
169. Reynolds, H.M. and Puthran, J. and Doyle, A. and Jones, W. and Nielsen, P.M.F. and Nash, M.P. and Rajagopal, V. Mapping breast cancer between clinical x-ray and MR images. Comput Biomech Med. 2011; Proceedings Workshop Computational Biomechanics for Medicine 2009, London, UK: Springer:81-90.
170. Ajay Kapur, Jochen Krucker, Oliver Astley, Donald Buckley, Jeffrey W. Eberhard, Abdal M. Alyassin, Bernhard E. H. Claus, Kai E. Thomenius, Heather Myers, Michael Rumsey, Roger N. Johnson and Steve Karr. Fusion of digital mammography with breast ultrasound: a phantom study. Proc SPIE 2002; 4682:526.
171. Hopp T, Holzapfel M, Ruiter NV, Li C, Duric N. Registration of x-ray mammograms and three-dimensional speed of sound images of the female breast. Proc SPIE 2010; 7629:4.
172. Irwin MR, Downey DB, Gardi L, Fenster A. Registered 3-D ultrasound and digital stereotactic mammography for breast biopsy guidance. IEEE Trans Med Imaging. 2008;27(3):391-401.
173. Rizzatto G, Fausto A. Breast imaging and volume navigation: MR imaging and ultrasound coregistration. Ultrasound Clin. 2009;4(3):261-71.
174. Piron CA, Causer P, Jong R, Shumak R, Plewes DB. A hybrid breast biopsy system combining ultrasound and MRI. IEEE Trans Med Imaging. 2003;22(9):1100-10.
175. Nakano, Shogo and Yoshida, Miwa and Fujii, Kimihito and Yorozuya, Kyoko and Mouri, Yukako and Kousaka, Junko and Fukutomi, Takashi and Kimura, Junko and Ishiguchi, Tsuneo and Ohno, Kazuko and Mizumoto, Takao and Harao, Michiko. Fusion of MRI and sonography image for breast cancer evaluation using real-time virtual sonography with magnetic navigation: first experience. Jpn J Clin Oncol. 2009;39(9):552-9.
176. Tang AM, Kacher DF, Lam EY, Wong KK, Jolesz FA, Yang ES. Simultaneous ultrasound and MRI system for breast biopsy: compatibility assessment and demonstration in a dual modality phantom. IEEE Trans Med Imaging. 2008;27(2):247-54.
177. Causer PA, Piron CA, Jong RA, Plewes DB. Preliminary in vivo validation of a dedicated breast MRI and sonographic coregistration imaging system. Am J Roentgenol. 2008;191(4):1203-7.
178. Tanner C, Karssemeijer N, Szekely G. Deformation models for registering MR and 3D ultrasound breast images. Proceedings of IEEE International Symposium on Biomedical Imaging. Piscataway, NJ: IEEE; 2011. p. 582-5.
179. Schirrmeister H, Kühn T, Guhlmann A, Santjohanser C, Hörster T, Nüssle K, Koretz K, Glatting G, Rieber A, Kreienberg R, Buck AC, Reske SN. Fluorine-18 2-deoxy-2-fluoro-D-glucose PET in the preoperative staging of breast cancer: comparison with the standard staging procedures. Eur J Nucl Med Molecular Imaging. 2001;28(3):351-8.
180. Tatsumi M, Cohade C, Mourtzikos KA, Fishman EK, Wahl RL. Initial experience with FDG-PET/CT in the evaluation of breast cancer. Eur J Nucl Med Mol Imaging. 2006;33(3):254-62.
181. Bergman, A.M. and Thompson, C.J. and Murthy, K. and Robar, J.L. and Clancy, R.L. and English, M.J. and Loutfi, A. and Lisbona, R. and Gagnon, J. Technique to obtain positron emission mammography images in registration with x-ray mammograms. Med Phys. 1998;25:2119-29.
182. Raymond R. Raylman and Stan Majewski and S. Sendhil Velan and Susan Lemieux and Brian Kross and Vladimir Popov and Mark F. Smith and Andrew G. Weisenberger. Simultaneous acquisition of magnetic resonance spectroscopy (MRS) data and positron emission tomography (PET) images with a prototype MR-compatible, small animal PET imager. J Magn Reson. 2007;186(2):305-10.
183. M.Z. Unlu and A. Krol and A. Magri and J.A. Mandel and W. Lee and K.G. Baum and E.D. Lipson and I.L. Coman and D.H. Feiglin. Computerized method for nonrigid MR-to-PET breast-image registration. Comput Biol Med. 2010;40(1):37-53.
184. Walvoord D, Baum KG, Helguera M, Krol A, Easton R. Localization of fiducial skin markers in MR images using correlation pattern recognition for PET/MRI nonrigid breast image registration. Proceedings of 37th IEEE Applied Imagery Pattern Recognition Workshop. Piscataway, NJ: IEEE; 2008. p. 1-4.
185. Coman, I. and Krol, A. and Feiglin, D. and Lipson, E. and Mandel, J. and Baum, K. and Unlu, M. and Li, W. Intermodality nonrigid breast-image registration. Proceedings of IEEE International Symposium on Biomedical Imaging. Piscataway, NJ: IEEE; 2004. p. 1440-2.
186. Krol, A. and Unlu, M.Z. and Baum, K.G. and Mandel, J.A. and Lee, W. and Coman, I.L. and Lipson, E.D. and Feiglin, D.H. MRI/PET nonrigid breast-image registration using skin fiducial markers. Phys Med. 2006;21:39-43.
187. Baum, K.G. and Helguera, M. and Hornak, J.P. and Kerekes, J.P. and Montag, E.D. and Unlu, M.Z. and Feiglin, D.H. and Krol, A. Techniques for fusion of multimodal images: Application to breast imaging. IEEE International Conference on Image Processing. Piscataway, NJ: IEEE; 2006. p. 2521-4.
188. Brooksby, B. and Jiang, S. and Dehghani, H. and Pogue, B.W. and Paulsen, K.D. and Kogel, C. and Doyley, M. and Weaver, J.B. and Poplack, S.P. Magnetic resonance-guided near-infrared tomography of the breast. Rev Sci Instrum. 2004;75:5262-70.
189. Konecky SD, Choe R, Corlu A, Lee K, Wiener R, Srinivas SM, Saffer JR, Freifelder R, Karp JS, Hajjioui N, Azar F, Yodh AG. Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography. Med Phys. 2008;35:446-55.
190. van de Ven, Stephanie and Elias, Sjoerd and Wiethoff, Andrea and van der Voort, Marjolein and Nielsen, Tim and Brendel, Bernhard and Bontus, Claas and Uhlemann, Falk and Nachabe, Rami and Harbers, Rik and van Beek, Michiel and Bakker, Leon and van der Mark, Martin and Luijten, Peter and Mali, Willem. Diffuse optical tomography of the breast: preliminary findings of a new prototype and comparison with magnetic resonance imaging. Eur Radiol. 2009;19:1108-13.
191. Zhang W, Wessel C, Ho CPS, Brady M. Non-rigid registration of breast images using feature points. Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE; 2010. p. 4387-90.
192. Rose CJ, Taylor CJ. A Generative Statistical Model of Mammographic Appearance. Proceedings of Medical Image Understanding and Analysis. Rueckert D, Hajnal J, Yang GZ, editors. Piscataway, NJ: IEEE; 2004. p. 89-92.
193. Dance, DR and Hunt, RA and Bakic, PR and Maidment, ADA and Sandborg, M. and Ullman, G. and Alm Carlsson, G. Breast dosimetry using high-resolution voxel phantoms. Radiat Protect Dosimetry. 2005;114(1-3):359-63.
194. Bliznakova K, Kolitsi Z, Pallikarakis N. Dual-energy mammography: simulation studies. Phys Med Biol. 2006;51(18):4497-4515.
195. Azar FS, Metaxas DN, Schnall MD. A finite element model of the breast for predicting mechanical deformations during biopsy procedures. Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. Piscataway, NJ: IEEE; 2000. p. 38-45.
196. Kellner AL, Nelson TR, Cervino LI, Boone JM. Simulation of mechanical compression of breast tissue. IEEE Trans Biomed Eng. 2007;54(10):1885-91.
197. Li CM, Segars WP, Tourassi GD, Boone JM, Dobbins III JT. Methodology for generating a 3D computerized breast phantom from empirical data. Med Phys. 2009;36(7):3122-31.
198. Tzu-Ching Shih and Jeon-Hor Chen and Dongxu Liu and Ke Nie and Lizhi Sun and Muqing Lin and Daniel Chang and Orhan Nalcioglu and Min-Ying Su. Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images. Phys Med Biol. 2010;55(14):4153-68.
199. Zyganitidis C, Bliznakova K, Pallikarakis N. A novel simulation algorithm for soft tissue compression. Med Biol Eng Comput. 2007;45(7):661-9.
200. Bliznakova K, Kazakli S, Pallikarakis N. An optimised 3D breast phantom for x-ray breast imaging techniques. Proceedings of 4th European Conference of the International Federation for Medical and Biological Engineering. New York: Springer; 2009. p. 2455-8.
201. Bliznakova K, Kolitsi Z, Speller R, Horrocks J, Tromba G, Pallikarakis N. Evaluation of digital breast tomosynthesis reconstruction algorithms using synchrotron radiation in standard geometry. Med Phys. 2010;37:1893-1903.
202. Ma AKW, Gunn S, Darambara DG. Introducing DeBRa: a detailed breast model for radiological studies. Phys Med Biol. 2009;54:4533-45.
203. Snoeren PR, Karssemeijer N. Thickness correction of mammographic images by means of a global parameter model of the compressed breast. IEEE Trans Med Imaging. 2004;23(7):799-806.
204. Bakic PR, Albert M, Brzakovic D, Maidment ADA. Mammogram synthesis using a 3D simulation. I. breast tissue model and image acquisition simulation. Med Phys. 2002;29:2131-9.
205. Bliznakova K, Bliznakov Z, Bravou V, Kolitsi Z, Pallikarakis N. A three-dimensional breast software phantom for mammography simulation. Phys Med Biol. 2003;48(22):3699-3719.
206. Gong X, Glick SJ, Liu B, Vedula AA, Thacker S. A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. Med Phys. 2006;33:1041-52.
207. Bakic PR, Zhang C, Maidment ADA. Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. Med Phys. 2011;38:3165-76.
208. Baiyu Chen and Jamie Shorey and Robert S. Saunders Jr. and Samuel Richard and John Thompson and Loren W. Nolte and Ehsan Samei. An anthropomorphic breast model for breast imaging simulation and optimization. Acad Radiol. 2011;18(5):536-46.
209. Duric N, Littrup P, Babkin A, Chambers D, Azevedo S, Pevzner R, Tokarev M, Holsapple E, Rama O, Duncan R. Development of ultrasound tomography for breast imaging: technical assessment. Med Phys. 2005;32:1375-6.
210. Gemmeke H, Ruiter NV. 3D ultrasound computer tomography for medical imaging. Nucl Instrum Methods Phys Res Sect A. 2007;580(2):1057-65.
211. Ruiter NV, Schwarzenberg GF, Zapf M, Gemmeke H. Improvement of 3D ultrasound computer tomography images by signal pre-processing. Proceedings of Ultrasonics Symposium, 2008. Piscataway, NJ: IEEE; 2008. p. 852-5.
212. Lindfors KK, Boone JM, Nelson TR, Yang K, Kwan ALC, Miller DWF. Dedicated breast CT: initial clinical experience. Radiololgy. 2008;246(3):725-33.
213. Rajagopal, V. and Lee, A. and Chung, J.H. and Warren, R. and Highnam, R.P. and Nash, M.P. and Nielsen, P.M.F. Creating individual-specific biomechanical models of the breast for medical image analysis1. Acad Radiol. 2008;15(11):1425-36.
214. Dick C, Georgii J, Westermann R. A real-time multigrid finite hexahedra method for elasticity simulation using CUDA. Simul Model Pract Theory. 2011;19(2):801-16.
215. Wang L, Filippatos K, Friman O, Hahn HK. Fully automated segmentation of the pectoralis muscle boundary in breast MR images. Proc SPIE 2011; 7963:796309.
216. Grand Challenges in Medical Image Analysis; 2012; http://www.grand-challenge.org.
217. Christensen, G. and Geng, X. and Kuhl, J. and Bruss, J. and Grabowski, T. and Pirwani, I. and Vannier, M. and Allen, J. and Damasio, H. Introduction to the non-rigid image registration evaluation project (NIREP). Proceedings of Workshop on Biomedical Image Registration. New York: Springer; 2006. p. 128-135.