Background: Digital breast tomosynthesis (DBT) reconstructs planar slices of the breast based on two-dimensional angular projections. Early studies and clinical trials show that DBT is an improvement over full field digital mammography (FFDM) because it provides the radiologist with better image quality and more information.
Objective: This paper presents a simulation system to model the performance of a slot-scanning FFDM and DBT system.
Methods: A tissue-equivalent three dimensional (3D) breast phantom was constructed, validated for slot-scanning digital mammography and used in simulating digital breast tomosynthesis. The simulation system was validated by comparing images acquired with a slot-scanning mammography machine with simulated phantom images, using the edge-test method and image quality metrics modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE). Different two-dimensional (2D) projections of the 3D phantom were simulated and the phantom was reconstructed using filtered backprojection.
Results: Image quality metrics showed equivalence between simulated and real images.
Conclusions: The simulation tool is suitable for slot-scanning FFDM and DBT and may be used for the design and comparison of mammography systems.
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http://dx.doi.org/10.3233/XST-160543 | DOI Listing |
Sci Rep
December 2024
Department of Health Disparities Research, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Black women (BW) experience age-adjusted breast cancer mortality rates that are 40% higher than White women. Although, screening rates for breast cancer are similar between White and Black women, differences in mammography utilization exist among women with lower socioeconomic status (SES). Moreover, perceived everyday discrimination (PED) has been shown to have an inverse relationship on health screening behavior among BW.
View Article and Find Full Text PDFSci Rep
December 2024
Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Department of Surgery, University of Vermont, Burlington.
Importance: The 2009 US Preventive Services Task Force breast cancer screening guideline changes led to decreases in screening mammography, raising concern about potential increases in late-stage disease and more invasive surgical treatments.
Objective: To investigate the incidence of breast cancer by stage at diagnosis and surgical treatment before and after the 2009 guideline changes.
Design, Setting, And Participants: This population-based, epidemiologic cohort study of women aged 40 years or older used 2004 to 2019 data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program.
Tomography
December 2024
Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
View Article and Find Full Text PDFJ Imaging
December 2024
Computer Science and Engineering Department, College of Engineering, University of Nevada, Reno, Main Campus, Reno, NV 89557, USA.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches.
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