330 related articles for article (PubMed ID: 25563259)
21. Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.
Gao M; Fessler JA; Chan HP
IEEE Trans Med Imaging; 2021 Jul; 40(7):1805-1816. PubMed ID: 33729933
[TBL] [Abstract][Full Text] [Related]
22. Detector Blur and Correlated Noise Modeling for Digital Breast Tomosynthesis Reconstruction.
Zheng J; Fessler JA; Chan HP
IEEE Trans Med Imaging; 2018 Jan; 37(1):116-127. PubMed ID: 28767366
[TBL] [Abstract][Full Text] [Related]
23. Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.
Gao M; Fessler JA; Chan HP
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37988758
[No Abstract] [Full Text] [Related]
24. The effect of angular dose distribution on the detection of microcalcifications in digital breast tomosynthesis.
Hu YH; Zhao W
Med Phys; 2011 May; 38(5):2455-66. PubMed ID: 21776781
[TBL] [Abstract][Full Text] [Related]
25. Impact of total variation minimization in volume rendering visualization of breast tomosynthesis data.
Mota AM; Clarkson MJ; Almeida P; Peralta L; Matela N
Comput Methods Programs Biomed; 2020 Oct; 195():105534. PubMed ID: 32480190
[TBL] [Abstract][Full Text] [Related]
26. Patchwork reconstruction with resolution modeling for digital breast tomosynthesis.
Michielsen K; Van Slambrouck K; Jerebko A; Nuyts J
Med Phys; 2013 Mar; 40(3):031105. PubMed ID: 23464285
[TBL] [Abstract][Full Text] [Related]
27. Synthesizing mammogram from digital breast tomosynthesis.
Wei J; Chan HP; Helvie MA; Roubidoux MA; Neal CH; Lu Y; Hadjiiski LM; Zhou C
Phys Med Biol; 2019 Feb; 64(4):045011. PubMed ID: 30625429
[TBL] [Abstract][Full Text] [Related]
28. Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis.
Samala RK; Chan HP; Hadjiiski LM; Helvie MA
Phys Med Biol; 2016 Oct; 61(19):7092-7112. PubMed ID: 27648708
[TBL] [Abstract][Full Text] [Related]
29. Task-based detectability in anatomical background in digital mammography, digital breast tomosynthesis and synthetic mammography.
Monnin P; Damet J; Bosmans H; Marshall NW
Phys Med Biol; 2024 Jan; 69(2):. PubMed ID: 38214048
[No Abstract] [Full Text] [Related]
30. Digital Breast Tomosynthesis: Physics, Artifacts, and Quality Control Considerations.
Tirada N; Li G; Dreizin D; Robinson L; Khorjekar G; Dromi S; Ernst T
Radiographics; 2019; 39(2):413-426. PubMed ID: 30768362
[TBL] [Abstract][Full Text] [Related]
31. Optimal photon energy comparison between digital breast tomosynthesis and mammography: a case study.
Di Maria S; Baptista M; Felix M; Oliveira N; Matela N; Janeiro L; Vaz P; Orvalho L; Silva A
Phys Med; 2014 Jun; 30(4):482-8. PubMed ID: 24613514
[TBL] [Abstract][Full Text] [Related]
32. Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction.
Garrett JW; Li Y; Li K; Chen GH
Med Phys; 2018 May; 45(5):2009-2022. PubMed ID: 29542821
[TBL] [Abstract][Full Text] [Related]
33. Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization.
Zhao C; Kanicki J; Konstantinidis AC; Patel T
Med Phys; 2015 Nov; 42(11):6294-308. PubMed ID: 26520722
[TBL] [Abstract][Full Text] [Related]
34. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
Chan HP; Wu YT; Sahiner B; Wei J; Helvie MA; Zhang Y; Moore RH; Kopans DB; Hadjiiski L; Way T
Med Phys; 2010 Jul; 37(7):3576-86. PubMed ID: 20831065
[TBL] [Abstract][Full Text] [Related]
35. Digital breast tomosynthesis: observer performance of clustered microcalcification detection on breast phantom images acquired with an experimental system using variable scan angles, angular increments, and number of projection views.
Chan HP; Goodsitt MM; Helvie MA; Zelakiewicz S; Schmitz A; Noroozian M; Paramagul C; Roubidoux MA; Nees AV; Neal CH; Carson P; Lu Y; Hadjiiski L; Wei J
Radiology; 2014 Dec; 273(3):675-85. PubMed ID: 25007048
[TBL] [Abstract][Full Text] [Related]
36. A novel pre-processing technique for improving image quality in digital breast tomosynthesis.
Kim H; Lee T; Hong J; Sabir S; Lee JR; Choi YW; Kim HH; Chae EY; Cho S
Med Phys; 2017 Feb; 44(2):417-425. PubMed ID: 28032909
[TBL] [Abstract][Full Text] [Related]
37. Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis.
Xu S; Lu J; Zhou O; Chen Y
Med Phys; 2015 Sep; 42(9):5377-90. PubMed ID: 26328987
[TBL] [Abstract][Full Text] [Related]
38. Comparison of digital breast tomosynthesis and 2D digital mammography using a hybrid performance test.
Cockmartin L; Marshall NW; Van Ongeval C; Aerts G; Stalmans D; Zanca F; Shaheen E; De Keyzer F; Dance DR; Young KC; Bosmans H
Phys Med Biol; 2015 May; 60(10):3939-58. PubMed ID: 25909596
[TBL] [Abstract][Full Text] [Related]
39. Optimization of the key imaging parameters for detection of microcalcifications in a newly developed digital breast tomosynthesis system.
Park HS; Kim YS; Kim HJ; Choi JG; Choi YW
Clin Imaging; 2013; 37(6):993-9. PubMed ID: 23891226
[TBL] [Abstract][Full Text] [Related]
40. [Assessment of imaging performance of digital breast tomosynthesis based on systematic simulation].
Deng Y; Zhu M; Li S; Wang Y; Gao Y; Ma J
Nan Fang Yi Ke Da Xue Xue Bao; 2021 Jun; 41(6):898-908. PubMed ID: 34238743
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]