Modeling the x-ray energy characteristics of DQE for full-field digital mammography

2001 ◽  
Author(s):  
J. Eric Tkaczyk ◽  
James W. LeBlanc ◽  
Robert L. Nevin ◽  
Gregory M. Kautz ◽  
Douglas Albagli ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Energy Characteristics ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography

Effective x-ray attenuation measurements with full field digital mammography

2006 ◽  
Vol 33 (11) ◽  
pp. 4350-4366 ◽  
Author(s):  
John J. Heine ◽  
Madhusmita Behera
Keyword(s):  
Digital Mammography ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography

scholarly journals X-Ray Induced Formation of γ-H2AX Foci after Full-Field Digital Mammography and Digital Breast-Tomosynthesis

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e70660 ◽  
Author(s):  
Siegfried A. Schwab ◽  
Michael Brand ◽  
Ina-Kristin Schlude ◽  
Wolfgang Wuest ◽  
Martina Meier-Meitinger ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography

X-ray scattering in full-field digital mammography

2003 ◽  
Vol 30 (7) ◽  
pp. 1864-1873 ◽  
Author(s):  
Kirsi Nykänen ◽  
Samuli Siltanen
Keyword(s):  
Digital Mammography ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography ◽  
X Ray Scattering ◽  
Ray Scattering

scholarly journals Image Quality Assessment via Segmentation of Breast Lesion in X-ray and Ultrasound Phantom Images from Fischer's Full Field Digital Mammography and Ultrasound (FFDMUS) System

2005 ◽  
Vol 4 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Jasjit Suri ◽  
Yujun Guo ◽  
Cara Coad ◽  
Tim Danielson ◽  
Idris Elbakri ◽  
...  
Keyword(s):  
Image Quality ◽  
Digital Mammography ◽  
Lesion Detection ◽  
Average Error ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography ◽  
The Mean

Fischer has been developing a fused full-field digital mammography and ultrasound (FFDMUS) system funded by the National Institute of Health (NIH). In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (i) to assess the image quality of X-ray and ultrasound images; (ii) to register multi-modality images; and (iii) to establish an automatic lesion detection methodology to assist the radiologist. In this paper we developed lesion segmentation strategies for ultrasound and X-ray images acquired using FFDMUS. For ultrasound lesion segmentation, a signal-to-noise (SNR)-based method was adapted. For X-ray segmentation, we used gradient vector flow (GVF)-based deformable model. The performance of these segmentation algorithms was evaluated. We also performed partial volume correction (PVC) analysis on the segmentation of ultrasound images. For X-ray lesion segmentation, we also studied the effect of PDE smoothing on GVF's ability to segment the lesion. We conclude that ultrasound image qualities from FFDMUS and Hand-Held ultrasound (HHUS) are comparable. The mean percentage error with PVC was 4.56% (4.31%) and 6.63% (5.89%) for 5 mm lesion and 3 mm lesion respectively. The mean average error from the segmented X-ray images with PDE yielded an average error of 9.61%. We also tested our program on synthetic datasets. The system was developed for Linux workstation using C/C++.

X-ray spectrum optimization of full-field digital mammography: Simulation and phantom study

2006 ◽  
Vol 33 (11) ◽  
pp. 4337-4349 ◽  
Author(s):  
Philipp Bernhardt ◽  
Thomas Mertelmeier ◽  
Martin Hoheisel
Keyword(s):  
Digital Mammography ◽  
Phantom Study ◽  
Full Field ◽  
X Ray ◽  
Full Field Digital Mammography ◽  
Spectrum Optimization
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