Objective: The purpose of this study was to achieve 3D registration of digital tomosynthesis mammographic volumes using mutual information.
Conclusion: Registration of digital breast tomosynthesis mammographic volumes was achieved with an average error of 1.8 +/- 1.4 mm.
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http://dx.doi.org/10.2214/AJR.08.1388 | 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 PDFBreast 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
March 2025
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.
View Article and Find Full Text PDFMed Phys
November 2024
Department of Imaging and Pathology, Division of Medical Physics & Quality Assessment, KU Leuven, Leuven, Belgium.
Background: Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications.
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