Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
/content/aapm/journal/medphys/42/7/10.1118/1.4919771
1.
1.R. E. Hendrick et al., Mammography Quality Control Manual (American College of Radiology, Reston, VA, 1999).
2.
2.R. E. van Engen et al., “Digital mammography update. European protocol for the quality control of the physical and technical aspects of mammography screening. S1, Part 1: Acceptance and constancy testing,” European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed., edited by N. Perry et al. (European Commission, Belgium, 2013).
3.
3.W. Huda, A. M. Sajewicz, K. M. Ogden, and D. R. Dance, “Experimental investigation of the dose and image quality characteristics of a digital mammography imaging system,” Med. Phys. 30(3), 442448 (2003).
http://dx.doi.org/10.1118/1.1543572
4.
4.M. B. Williams et al., “Optimization of exposure parameters in full field digital mammography,” Med. Phys. 35(6), 24142423 (2008).
http://dx.doi.org/10.1118/1.2912177
5.
5.C. M. Kuzmiak et al., “Comparison of full-field digital mammography to screen-film mammography with respect to contrast and spatial resolution in tissue equivalent breast phantoms,” Med. Phys. 32(10), 31443150 (2005).
http://dx.doi.org/10.1118/1.2040710
6.
6.L. T. Niklason et al., “Digital tomosynthesis in breast imaging,” Radiology 205(2), 399406 (1997).
http://dx.doi.org/10.1148/radiology.205.2.9356620
7.
7.T. Wu, R. H. Moore, E. A. Rafferty, and D. B. Kopans, “A comparison of reconstruction algorithms for breast tomosynthesis,” Med. Phys. 31(9), 26362647 (2004).
http://dx.doi.org/10.1118/1.1786692
8.
8.A. Tucker et al., “Optimizing configuration parameters of a stationary digital breast tomosynthesis system based on carbon nanotube x-ray sources,” Proc. SPIE 8313, 831307 (2012).
http://dx.doi.org/10.1117/12.911530
10.
10.Y. Lu, B. Lau, Y. Hu, W. Zhao, and G. Gindi, “A simple scatter correction method for dual energy contrast-enhanced digital breast tomosynthesis,” Proc. SPIE 9033, 903344 (2014).
http://dx.doi.org/10.1117/12.2043190
11.
11.C. C. Brunner et al., “Evaluation of various mammography phantoms for image quality assessment in digital breast tomosynthesis,” Breast Imaging, Lecture Notes in Computer Science Vol. 7361 (Springer, Berlin, 2012), pp. 284291.
12.
12.S. Vecchio, A. Albanese, P. Vignolio, and A. Taibi, “A novel approach to digital breast tomosynthesis for simultaneous acquisition of 2D and 3D images,” Eur. Radiol. 21(6), 12071213 (2011).
http://dx.doi.org/10.1007/s00330-010-2041-y
13.
13.R. Mahadevan, L. C. Ikejimba, Y. Lin, E. Samei, and J. Y. Lo, “A task-based comparison of two reconstruction algorithms for digital breast tomosynthesis,” Proc. SPIE 9033, 903324 (2014).
http://dx.doi.org/10.1117/12.2043829
14.
14.L. C. Ikejimba et al., “Task-based strategy for optimized contrast enhanced breast imaging: Analysis of six imaging techniques for mammography and tomosynthesis,” Med. Phys. 41(6), 061908 (14pp.) (2014).
http://dx.doi.org/10.1118/1.4873317
15.
15.L. Cockmartin, H. Bosmans, and N. W. Marshall, “Comparative power law analysis of structured breast phantom and patient images in digital mammography and breast tomosynthesis,” Med. Phys. 40(8), 081920(17pp.) (2013).
http://dx.doi.org/10.1118/1.4816309
16.
16.B. Chen et al., “An anthropomorphic breast model for breast imaging simulation and optimization,” Acad. Radiol. 18(5), 536546 (2011).
http://dx.doi.org/10.1016/j.acra.2010.11.009
17.
17.C. M. Li, W. P. Segars, J. T. Dobbins III, G. D. Tourassi, and J. M. Boone, “Methodology for generating a 3D computerized breast phantom from empirical data,” Med. Phys. 36(7), 31223131 (2009).
http://dx.doi.org/10.1118/1.3140588
18.
18.C. M. L. Hsu, M. L. Palmeri, W. P. Segars, J. T. Dobbins III, and A. I. Veress, “An analysis of the mechanical parameters used for finite element compression of a high-resolution 3D breast phantom,” Med. Phys. 38(10), 57565770 (2011).
http://dx.doi.org/10.1118/1.3637500
19.
19.C. M. L. Hsu, M. L. Palmeri, W. P. Segars, A. I. Veress, and J. T. Dobbins III, “Generation of a suite of 3D computer-generated breast phantoms from a limited set of human subject data,” Med. Phys. 40(4), 043703(11pp.) (2013).
http://dx.doi.org/10.1118/1.4794924
20.
20.P. R. Bakic, M. Albert, D. Brzakovic, and A. D. A. Maidment, “Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation,” Med. Phys. 29(9), 21312139 (2002).
http://dx.doi.org/10.1118/1.1501143
21.
21.P. R. Bakic, M. Albert, D. Brzakovic, and A. D. A. Maidment, “Mammogram synthesis using a 3D simulation. II. Evaluation of synthetic mammogram texture,” Med. Phys. 29(9), 21402151 (2002).
http://dx.doi.org/10.1118/1.1501144
22.
22.P. R. Bakic, M. Albert, D. Brzakovic, and A. D. A. Maidment, “Mammogram synthesis using a three-dimensional simulation. III. Modeling and evaluation of the breast ductal network,” Med. Phys. 30(7), 19141925 (2003).
http://dx.doi.org/10.1118/1.1586453
23.
23.K. Bliznakova, Z. Bliznakova, V. Bravou, Z. Kolitsi, and N. Pallikarakis, “A three-dimensional breast software phantom for mammography simulation,” Phys. Med. Biol. 48(22), 36993719 (2003).
http://dx.doi.org/10.1088/0031-9155/48/22/006
24.
24.P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38(6), 31653176 (2011).
http://dx.doi.org/10.1118/1.3590357
25.
25.B. A. Lau, I. Reiser, R. M. Nishikawa, and P. R. Bakic, “A statistically defined anthropomorphic software breast phantom,” Med. Phys. 9(6), 33753385 (2012).
http://dx.doi.org/10.1118/1.4718576
26.
26.J. M. O’Connor, M. Das, C. S. Dider, M. Mahd, and S. Glick, “Generation of voxelized breast phantoms from surgical mastectomy specimens,” Med. Phys. 40(4), 041915 (12pp.) (2013).
http://dx.doi.org/10.1118/1.4795758
27.
27.S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40(5), 051914 (15pp.) (2013).
http://dx.doi.org/10.1118/1.4800501
28.
28.N. Kiarashi et al., “Development and application of a suite of 4D virtual breast phantoms for optimization and evaluation of breast imaging systems,” IEEE Trans. Med. Imaging 33(7), 14011409 (2014).
http://dx.doi.org/10.1109/TMI.2014.2312733
29.
29.A. K. Carton, P. R. Bakic, C. Ullberg, H. Derand, and A. D. A. Maidment, “Development of a physical 3D anthropomorphic breast phantom,” Med. Phys. 38(2), 891896 (2011).
http://dx.doi.org/10.1118/1.3533896
30.
30.N. D. Prionas, G. W. Burkett, S. E. McKenney, L. Chen, R. L. Stern, and J. M. Boone, “Development of a patient-specific two-compartment anthropomorphic breast phantom,” Phys. Med. Biol. 57(13), 42934307 (2012).
http://dx.doi.org/10.1088/0031-9155/57/13/4293
31.
31.K. K. Lindfors, J. M. Boone, T. R. Nelson, K. Yang, A. L. C. Kwan, and D. F. Miller, “Dedicated breast CT: Initial clinical experience,” Radiology 246(3), 725733 (2008).
http://dx.doi.org/10.1148/radiol.2463070410
32.
32.W. E. Lorensen and H. E. Cline, “Marching cubes: A high-resolution 3D surface construction algorithm,” ACM SIGGRAPH Comput. Graphics 21, 163169 (1987).
http://dx.doi.org/10.1145/37402.37422
33.
33.T. Mertelmeier, J. Orman, W. Haerer, and M. K. Dudam, “Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device,” Proc. SPIE 6142, 61420F (2006).
http://dx.doi.org/10.1117/12.651380
34.
34.S. Vedantham and A. Karellas, “X-ray phase contrast imaging of the breast: Analysis of tissue simulating materials,” Med. Phys. 40(4), 041906(8pp.) (2013).
http://dx.doi.org/10.1118/1.4794503
35.
35.R. S. Saunders, E. Samei, J. L. Jesneck, and J. Y. Lo, “Physical characterization of a prototype selenium-based full field digital mammography detector,” Med. Phys. 32(2), 588599 (2005).
http://dx.doi.org/10.1118/1.1855033
36.
36.J. T. Dobbins III, E. Samei, N. T. Ranger, and Y. Chen, “Intercomparison of methods for image quality characterization. II. Noise power spectrum,” Med. Phys. 33(5), 14661475 (2006).
http://dx.doi.org/10.1118/1.2188819
37.
37.A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28(4), 419437 (2001).
http://dx.doi.org/10.1118/1.1355308
38.
38.A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: Effect of spectrum exponents,” J. Opt. Soc. Am. A 24(12), B52B60 (2007).
http://dx.doi.org/10.1364/josaa.24.000b52
39.
39.D. R. White, R. J. Martin, and R. Darlison, “Epoxy resin based tissue substitutes,” Br. J. Radiol. 50, 814821 (1977).
http://dx.doi.org/10.1259/0007-1285-50-599-814
40.
40.W. P. Segars et al., “Population of 100 realistic, patient-based computerized breast phantoms for multi-modality imaging research,” Proc. SPIE 9033, 90331X (2014).
http://dx.doi.org/10.1117/12.2043868
41.
41.A. Rashidnasab et al., “Simulation and assessment of realistic breast lesions using fractal growth models,” Phys. Med. Biol. 58(16), 56135627 (2013).
http://dx.doi.org/10.1088/0031-9155/58/16/5613
42.
42.E. Shaheen et al., “The simulation of 3D mass models in 2D digital mammography and breast tomosynthesis,” Med. Phys. 41, 081913(16pp.) (2014).
http://dx.doi.org/10.1118/1.4890590
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/42/7/10.1118/1.4919771
Loading
/content/aapm/journal/medphys/42/7/10.1118/1.4919771
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/42/7/10.1118/1.4919771
2015-06-15
2016-09-28

Abstract

Physical phantoms are essential for the development, optimization, and evaluation of x-ray breast imaging systems. Recognizing the major effect of anatomy on image quality and clinical performance, such phantoms should ideally reflect the three-dimensional structure of the human breast. Currently, there is no commercially available three-dimensional physical breast phantom that is anthropomorphic. The authors present the development of a new suite of physical breast phantoms based on human data.

The phantoms were designed to match the extended cardiac-torso virtual breast phantoms that were based on dedicated breast computed tomography images of human subjects. The phantoms were fabricated by high-resolution multimaterial additive manufacturing (3D printing) technology. The glandular equivalency of the photopolymer materials was measured relative to breast tissue-equivalent plastic materials. Based on the current state-of-the-art in the technology and available materials, two variations were fabricated. The first was a dual-material phantom, the . Fibroglandular tissue and skin were represented by the most radiographically dense material available; adipose tissue was represented by the least radiographically dense material. The second variation, the , was fabricated with a single material to represent fibroglandular tissue and skin. It was subsequently filled with adipose-equivalent materials including oil, beeswax, and permanent urethane-based polymer. Simulated microcalcification clusters were further included in the phantoms via crushed eggshells. The phantoms were imaged and characterized visually and quantitatively.

The mammographic projections and tomosynthesis reconstructed images of the fabricated phantoms yielded realistic breast background. The mammograms of the phantoms demonstrated close correlation with simulated mammographic projection images of the corresponding virtual phantoms. Furthermore, power-law descriptions of the phantom images were in general agreement with real human images. The Singlet approach offered more realistic contrast as compared to the Doublet approach, but at the expense of air bubbles and air pockets that formed during the filling process.

The presented physical breast phantoms and their matching virtual breast phantoms offer realistic breast anatomy, patient variability, and ease of use, making them a potential candidate for performing both system quality control testing and virtual clinical trials.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/42/7/1.4919771.html;jsessionid=EVQsjdH5uSjdfxYt-eAvrb06.x-aip-live-02?itemId=/content/aapm/journal/medphys/42/7/10.1118/1.4919771&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=online.medphys.org/42/7/10.1118/1.4919771&pageURL=http://scitation.aip.org/content/aapm/journal/medphys/42/7/10.1118/1.4919771'
Right1,Right2,Right3,