Digital mammography separates the processes of image acquisition, processing, and display, which allows for the optimization of each process. The result addresses some of the limitations of screen film mammography. This work reviews the advantages of the decoupling of the processes and the clinical trials comparing digital mammography with film-screen mammography in the screening setting. Advanced applications of digital mammography, such as contrast-enhanced digital mammography and tomosynthesis, are also discussed.
It has been well established that screening mammography reduces the rate of death from breast cancer in women 40 years of age and older ( ). Several large controlled trials have demonstrated screening mammography is efficacious and can reduce breast cancer mortality by 18%–30% ( ). Unfortunately, screening mammography does not detect all breast cancers. Screen-film mammography (SFM) fails to detect 10%–20% of palpable breast cancers, especially in dense breasts, in which there may be insufficient difference in contrast of the breast tissue and cancer ( ). Carney et al demonstrated that increased density was associated with decreased sensitivity of screening mammography ( ). In addition, interval cancers occur, which are mammographically undetectable cancers at the time of screening, yet become clinically or mammographically apparent before the next annual screening mammogram. Furthermore, there are missed cancers, which are those that have mammographic signs of malignancy that were either misinterpreted or not recognized on the preceding screening mammogram. Missed cancers occur among all radiologists, even those with extensive experience. Rates of missed cancers vary and can be influenced by the method of review. The percentage of missed cancers ranges from 1.3% to 35.9%, likely dependent on study design ( ). Bird et al reported significantly higher percentages of missed cancers in dense breasts ( ), but Birdwell et al reported missed cancer to be evenly distributed in mostly fatty and mostly dense breasts ( ).
Increased breast density is a moderate independent risk factor for breast cancer. The risk of breast cancer in women with increased density is four to six times that of women with less dense tissue ( ). Therefore, increased breast density is not only a risk factor for breast cancer, but is associated with decreased visibility and conspicuity of lesions on mammography.
Full-field digital mammography (DM) addresses some of the limitations of SFM. With DM, the processes of image acquisition, processing, and display are decoupled or separated. This allows for optimization of each process.
Image acquisition
With DM, the x-ray photons strike a digital detector, which converts the absorbed energy into an electronic signal. The signal is then received, processed, and stored as a matrix. The signal is linearly proportional to the transmitted x-ray intensity, which results in a wider dynamic range of the images than for screen-film images (1,000:1 compared with 40:1 of SFM) ( ). DM provides a broader dynamic range of densities and greater contrast resolution in dense breasts ( ). Spatial resolution is measured in terms of the smallest high-contrast objects, which can be distinguished as distinct. SFM resolves 12 and 15 line pairs per millimeter, which is equivalent to 42- to 30-μm pixels, respectively. The spatial resolution of DM ranges from 100- to about 50-μm pixels in whole breast mode or 5–10 line pairs per millimeter ( ). Therefore, spatial resolution is superior in SFM compared to DM. Digital images have lower noise than SFM because of reduction in quantum mottle and elimination of granular artifact from film emulsion ( ). Radiation dose of DM and SFM is comparable ( ).
Image processing
Another advantage of DM is the decoupling of processing, which enables post-acquisition processing of the image. This is advantageous over SFM because it can provide diagnostic information without requiring additional images or exposing the patient to additional radiation. The processing is designed to optimize the quality of the images. Processing tools may change the contrast and brightness of an image (ie, window and level) or enlarge part of or the entire breast. Because different parts of the breast may be viewed with different brightness and contrast settings, an optimal analysis of the fatty and dense components of the breast is made possible. Post-acquisition processing may compensate for problems of underexposure or overexposure. This can decrease the need for repeat views and subsequently expose the patient to less radiation. Manufacturers have developed different algorithms, specific to their systems, to optimize the images. These algorithms can change the intensity of window width and level automatically, depending on the range of intensity values present. Algorithms can enhance the sharpness of the borders of a lesion. Peripheral equalization algorithms can enhance the visualization of structures in the periphery of the breast. Other processing techniques include image inversion and noise suppression ( ).
Image display and interpretation
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Image storage
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Financial considerations
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Clinical trials
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Table 1
Outcome Metrics of Clinical Trials
Cancer Recall Rate (%) PPV (%) Cancer Detection Rate (%) Lewin ( ) Total 42 DM 25 11.8 3.4 0.37 SFM 33 14.9 3.3 0.49 Skaane ( ) Total 31 DM 23 4.6 12 0.62 SFM 28 3.5 20 0.76 Pisano ( ) Total 335 DM 185 8.4 5 0.43 SFM 174 8.4 5 0.41 Del Turco ( ) Total 188 DM 104 4.56 15.9 0.72 SFM 84 3.96 14.7 0.58
PPV: positive predictive value; DM: digital mammography.
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Advanced applications
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Computer-Aided Detection
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Telemammography
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Contrast-Enhanced DM
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Tomosynthesis
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Summary
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