The recent introduction of digital mammography represents a significant technologic advance in breast imaging. However, many radiologists and technologists are unfamiliar with artifacts that are commonly seen with this modality, and recognizing these artifacts is critical for optimizing image quality. Commonly encountered artifacts include patient-related artifacts (motion artifact, antiperspirant artifact, thin breast artifact), hardware-related artifacts (field inhomogeneity, detector-associated artifacts, collimator misalignment, underexposure, grid lines, grid misplacement, vibration artifact), and software processing artifacts ("breast-within-a-breast" artifact, vertical processing bars, loss of edge, high-density artifacts). Although some of these artifacts are similar to those seen with screen-film mammography, many are unique to digital mammography--specifically, those due to software processing errors or digital detector deficiencies. In addition, digital mammographic artifacts depend on detector technology (direct vs indirect) and therefore can be vendor specific. It is important that the technologist, radiologist, and physicist become familiar with the spectrum of digital mammographic artifacts and pay careful attention to digital quality control procedures to ensure optimal image quality.
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http://dx.doi.org/10.1148/rg.287085053 | DOI Listing |
Radiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
View Article and Find Full Text PDFEur J Radiol Open
June 2025
Radiology Department, National Cancer Institute, Cairo University, Egypt.
Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Methods And Materials: Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
Bone
December 2024
Bone and Joint Center, Henry Ford Health, Detroit, MI, USA; Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA. Electronic address:
Bone fractures due to osteoporosis are a significant problem. Limited accuracy of standard bone mineral density (BMD) for fracture risk assessment, combined with low adherence to bone health screening precludes identification of those at risk of fracture. Because of the wide availability of digital breast tomosynthesis (DBT) imaging, bone screening using a DBT scanner at the time of breast screening has been proposed.
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