What Is Mammography?

Mammography is a low-dose X-ray imaging technique used to examine breast tissue for the detection and diagnosis of breast disease, most significantly breast cancer. The modality uses specialized X-ray tubes that emit photons in the 20 to 35 kilovolt range, lower than general radiography, to maximize contrast between soft tissue structures with similar attenuation coefficients. Clinically, mammography is performed in two contexts: screening, which surveys asymptomatic populations on a periodic basis, and diagnostic imaging, which characterizes an identified abnormality in detail. It is one of the most studied and regulated medical imaging modalities, and its design and performance are influenced directly by physics, materials science, and signal processing research.

Imaging Physics and Detector Technology

A mammography system consists of an X-ray source, compression paddle, image receptor, and a dedicated gantry that positions the breast between the tube and detector. Film-screen systems dominated the field through the 1990s, but digital detectors have since become the clinical standard. Digital systems use either indirect detection, in which a scintillator converts X-rays to visible light before photodetection, or direct detection, in which amorphous selenium converts X-rays to charge directly in the detector layer. Digital acquisition allows immediate image display, postprocessing for contrast and edge enhancement, and electronic storage, which enables computer-aided detection workflows. The clinical performance of digital versus film mammography has been evaluated extensively, with digital systems offering advantages in women with dense breast tissue and in younger patients.

Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) extends conventional two-dimensional digital mammography into a quasi-three-dimensional acquisition. The X-ray tube moves through a limited arc, typically spanning 11 to 60 degrees, while acquiring multiple low-dose projection images. These projections are then reconstructed using algorithms related to computed tomography, including filtered back projection and maximum likelihood reconstruction, to produce thin image slices through the compressed breast. As described in studies of digital mammography and tomosynthesis, DBT reduces the masking effect of overlapping tissue structures, detecting up to 40 percent more masses than conventional planar mammography in early trials. Tomosynthesis reading times are longer, and managing the increased data volume requires substantial image management infrastructure. Current DBT systems operate within FDA dose limits of 300 millirads per acquisition.

Computer-Aided Detection and AI Integration

Computer-aided detection (CAD) systems analyze digitized mammographic images to flag regions of interest for radiologist review. First-generation CAD tools used handcrafted feature extraction, searching for characteristic patterns of masses, asymmetries, and clustered microcalcifications. More recent approaches apply convolutional neural networks trained on annotated imaging datasets, achieving sensitivities and specificities that in some studies approach or exceed those of individual radiologists. Deep learning methods applied to mammography have demonstrated particularly strong performance on large population screening datasets, where the volume of images makes fully manual review resource-intensive. Integration of AI tools into clinical workflows raises questions of regulatory approval, liability, and appropriate use, which standards bodies including the FDA and ACR are actively addressing.

Applications

Mammography has applications in a range of fields, including:

• Population breast cancer screening programs and health surveillance
• Diagnosis and characterization of palpable or detected breast abnormalities
• Guidance for percutaneous biopsy procedures
• Monitoring of treatment response in oncology
• Medical imaging systems research and detector development