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.
Improved digital breast tomosynthesis images using automated ultrasound
1. Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and x-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
2. M. Nothacker, V. Duda, M. Hahn, M. Warm, F. Degenhardt, H. Madjar, S. Weinbrenner, and U. Albert, “Early detection of breast cancer: Benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue,” Syst. Rev., Bmc Cancer 9(1), 335–343 (2009).
3. K. Kerlikowske, A. J. Cook, D. S. M. Buist, S. R. Cummings, C. Vachon, P. Vacek, and D. L. Miglioretti, “Breast cancer risk by breast density, menopause, and postmenopausal hormone therapy use,” J. Clin. Oncol. 28(24), 3830–3837 (2010).
6. I. Sechopoulos, “A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications,” Med. Phys. 40(1), 014302 (17pp.) (2013).
8. J. Lei, P. Yang, L. Zhang, Y. Wang, and K. Yang, “Diagnostic accuracy of digital breast tomosynthesis versus digital mammography for benign and malignant lesions in breasts: A meta-analysis,” Eur. Radiol. 24(3), 595–602 (2014).
9. I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: A comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18(12), 2817–2825 (2008).
10. D. Gur, G. S. Abrams, D. M. Chough, M. A. Ganott, C. M. Hakim, R. L. Perrin, G. Y. Rathfon, J. H. Sumkin, M. L. Zuley, and A. I. Bandos, “Digital breast tomosynthesis: Observer performance study,” Am. J. Roentgenol. 193(2), 586–591 (2009).
11. M. Noroozian, L. Hadjiiski, S. Rahnama-Moghadam, K. A. Klein, D. O. Jeffries, R. W. Pinsky, H.-P. Chan, P. L. Carson, M. A. Helvie, and M. A. Roubidoux, “Digital breast tomosynthesis is comparable to mammographic spot views for mass characterization,” Radiology 262(1), 61–68 (2012).
12. T. Wu, A. Stewart, M. Stanton, T. McCauley, W. Phillips, D. B. Kopans, R. H. Moore, J. W. Eberhard, B. Opsahl-Ong, L. Niklason, and M. B. Williams, “Tomographic mammography using a limited number of low-dose cone-beam projection images,” Med. Phys. 30, 365–380 (2003).
13. B. E. H. Claus and J. W. Eberhard, “Generalized filtered back-projection reconstruction in digital tomosynthesis,” U.S. patent 6,707,878[P] (16 March 2004).
14. T. Wu, R. H. Moore, and D. B. Kopans, “Voting strategy for artifact reduction in digital breast tomosynthesis,” Med. Phys. 33, 2461–2471 (2006).
15. Y. H. Hu, W. Zhao, T. Mertelmeier, and J. Ludwig, “Image artifact in digital breast tomosynthesis and its dependence on system and reconstruction parameters,” Digital Mammography (Springer, Berlin, 2008), pp. 628–634.
16. Y. H. Hu, B. Zhao, and W. Zhao, “Image artifacts in digital breast tomosynthesis: Investigation of the effects of system geometry and reconstruction parameters using a linear system approach,” Med. Phys. 35, 5242–5252 (2008).
17. K. M. Kelly, J. Dean, W. S. Comulada, and S. J. Lee, “Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts,” Eur. Radiol. 20(3), 734–742 (2010).
18. K. M. Kelly, J. Dean, S. J. Lee, and W. S. Comulada, “Breast cancer detection: Radiologists’ performance using mammography with and without automated whole-breast ultrasound,” Eur. Radiol. 20(11), 2557–2564 (2010).
19. K. M. Kelly and G. A. Richwald, “Automated whole-breast ultrasound: Advancing the performance of breast cancer screening,” Semin. Ultrasound CT MRI 32(4), 273–280 (2011).
20. S. Sinha, F. M. Hooi, R. Pinsky, O. Kripfgans, and P. Carson, “Image processing and registration of opposed view 3d breast ultrasound,” Breast Imaging (Springer, Berlin, 2012), pp. 666–672.
21. M. M. Goodsitt, H. P. Chan, L. Hadjiiski, G. L. LeCarpentier, and P. L. Carson, “Automated registration of volumes of interest for a combined x-ray tomosynthesis and ultrasound breast imaging system,” Digital Mammography (Springer, Berlin, 2008), Vol. 5116, pp. 463–468.
22. W. F. Conway, C. W. Hayes, and W. H. Brewer, “Occult breast masses: Use of a mammographic localizing grid for US evaluation,” Radiology 181(1), 143–146 (1991).
23. F. Padilla, M. A. Roubidoux, C. Paramagul, S. P. Sinha, M. M. Goodsitt, G. L. Le Carpentier, H. P. Chan, L. M. Hadjiiski, J. B. Fowlkes, A. D. Joe, K. A. Klein, A. V. Nees, M. Noroozian, S. K. Patterson, R. W. Pinsky, F. M. Hooi, and P. L. Carson, “Breast mass characterization using 3-dimensional automated ultrasound as an adjunct to digital breast tomosynthesis: A pilot study,” J. Ultrasound Med. 32(1), 93–104 (2013).
25. L. Li, Y. Xing, Z. Chen, L. Zhang, and K. Kang, “A curve-filtered FDK (C-FDK) reconstruction algorithm for circular cone-beam CT,” J. X-Ray Sci. Technol. 19(3), 355–371 (2011).
26. S. T. Schindera, L. Diedrichsen, H. C. Müller, O. Rusch, D. Marin, B. Schmidt, R. Raupach, P. Vock, and Z. Szucs-Farkas, “Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: Assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study,” Radiology 260(2), 454–462 (2011).
27. Y. Zhou, J. B. Thibault, C. A. Bouman, K. D. Sauer, and H. Jiang, “Fast model-based x-ray CT reconstruction using spatially nonhomogeneous ICD optimization,” IEEE Trans. Image Process 20(1), 161–175 (2011).
28. S. C. B. Lo and M. T. Freedman, “Image reconstruction of arc cone-beam CT with reprojection: A preliminary study,” Proc. SPIE 8668, 866831–1866831–8 (2013).
29. Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose CT reconstruction via edge-preserving total variation regularization,” Phys. Med. Biol. 56(18), 5949–5967 (2011).
30. A. Korna, M. Fenchela, B. Bendera, S. Danza, T. K. Hausera, D. Ketelsenb,T. Flohrc, C. D. Claussenb, M. Heuschmidb, U. Ernemanna, and H. Brodoefelb, “Iterative reconstruction in head CT: Image quality of routine and low-dose protocols in comparison with standard filtered back-projection,” Am. J. Neuroradiol. 33(2), 218–224 (2012).
32. X. Wan, F. Zhang, Q. Chu, F. Sun, B. Yuan, and Z. Liu, “Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography,” J. Struct. Biol. 175(3), 277–287 (2011).
33. T. Pengpan, W. Qiu, N. D. Smith, and M. Soleimani, “Cone beam CT using motion-compensated algebraic reconstruction methods with limited data,” Comput. Method Program Biomed. 105(3), 246–256 (2012).
34. A. H. Andersen and A. C. Kak, “Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm,” Ultras. Imaging 6(1), 81–94 (1984).
35. W. M. Pang, J. Qin, Y. Lu, Y. Xie, C. K. Chui, and P. A. Heng, “Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU,” Int. J. Comput. Assist. Radiol. Surg. 6(2), 187–199 (2011).
37. M. Jiang and G. Wang, “Development of iterative algorithms for image reconstruction,” J. X-Ray Sci. Technol. 10(1), 77–86 (2001).
38. Y. Yuan, “Step-sizes for the gradient method” Ams/Ip Stud. Adv. Math. 42(2), 785–796 (2008).
39. S. Sinha, F. M. Hooi, Z. Syed, and R. Pinsky, “Machine learning for noise removal on breast ultrasound images,” in Proceedings of the IEEE Ultrasonics Symposium (2010 IEEE, San Diego, CA, 2010), pp. 2020–2023.
40. S. Sinha, “Breast Cancer Detection on Automated 3D Ultrasound with Co-localized 3D X-ray,” (Doctoral dissertation, The University of Michigan, 2010).
41. E. L. Madsen, J. A. Zagzebski, and G. R. Frank, “An anthropomorphic ultrasound breast phantom containing intermediate-sized scatterers,” Ultrasound Med. Biol. 8(4), 381–392 (1982).
42. P. L. Carson, Z. Fouzaan, S. A. M. Verweij, and W. M. Lee, “Dual sided automated ultrasound system in the mammographic geometry,” IEEE Int. Ultrason. Symp. 1948(5719), 2134–2137 (2011).
43. H. Neemuchwala, A. Hero, S. Zabuawala, S. Zabuawala, and P. Carson, “Image registration methods in high-dimensional space,” Int. J. Imaging Syst. Technol. 16(5), 130–145 (2006).
44. G. Narayanasamy, G. L. LeCarpentier, M. Roubidoux, J. B. Fowlkes, A. F. Schott, and P. L. Carson, “Spatial registration of temporally separated whole breast 3D ultrasound images,” Med. Phys. 36(9), 4288–4300 (2009).
45. T. Wu, R. H. Moore, E. A. Rafferty, and D. B. Kopans, “A comparison of reconstruction algorithms for breast tomosynthesis,” Med. Phys. 31, 2636–2647 (2004).
46. Y. Zhang, H. P. Chan, B. Sahiner, J. Wei, M. M. Goodsitt, L. M. Hadjiiski, J. Ge, and C. Zhou, “A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis,” Med. Phys. 33, 3781–3795 (2006).
47. J. Zhou, B. Zhao, and W. Zhao, “A computer simulation platform for the optimization of a breast tomosynthesis system,” Med. Phys. 34, 1098–1109 (2007).
Article metrics loading...
Digital breast tomosynthesis (DBT) offers poor image quality along the depth direction. This paper presents a new method that improves the image quality of DBT considerably through thea priori information from automated ultrasound (AUS) images.
DBT and AUS images of a complex breast-mimicking phantom are acquired by a DBT/AUS dual-modality system. The AUS images are taken in the same geometry as the DBT images and the gradient information of the in-slice AUS images is adopted into the new loss functional during the DBT reconstruction process. The additional data allow for new iterative equations through solving the optimization problem utilizing the gradient descent method. Both visual comparison and quantitative analysis are employed to evaluate the improvement on DBT images. Normalized line profiles of lesions are obtained to compare the edges of the DBT and AUS-corrected DBT images. Additionally, image quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) are calculated to quantify the effectiveness of the proposed method.
In traditional DBT image reconstructions, serious artifacts can be found along the depth direction (Z direction), resulting in the blurring of lesion edges in the off-focus planes parallel to the detector. However, by applying the proposed method, the quality of the reconstructed DBT images is greatly improved. Visually, the AUS-corrected DBT images have much clearer borders in both in-focus and off-focus planes, fewer Z direction artifacts and reduced overlapping effect compared to the conventional DBT images. Quantitatively, the corrected DBT images have better ASF, indicating a great reduction in Z direction artifacts as well as better Z resolution. The sharper line profiles along the Y direction show enhancement on the edges. Besides, noise is also reduced, evidenced by the obviously improved SDNR values.
The proposed method provides great improvement on the quality of DBT images. This improvement makes it easier to locate and to distinguish a lesion, which may help improve the accuracy of the diagnosis using DBT imaging.
Full text loading...
Most read this month