Mammographic Signs of Malignancy: Impact of Digital Mammography on Visibility and Appearance
The two main aspects affecting lesion visibility in digital mammography are image noise and image processing. A high amount of noise in low-dose mammographic images adversely affects detection of subtle microcalcifications and characterization of mass les
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CONTENTS
KEY POINTS 11.1
Introduction 175
11.2 Mass Lesions 176 11.2.1 Determining the Presence of a True Mass Lesion 176 11.2.2 Distinguishing Benign and Malignant Mass Lesions 176 11.3 Calcifications 177 11.3.1 Detection of Subtle Microcalcifications 180 11.3.2 Classification of Benign and Malignant Breast Calcifications 181 11.4
Other Mammographic Signs of Malignancy 182 11.4.1 Architectural Distortions 182 11.4.2 Asymmetries 184 11.4.3 Associated Findings 184 References
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The two main aspects affecting lesion visibility in digital mammography are image noise and image processing. A high amount of noise in low-dose mammographic images adversely affects detection of subtle microcalcifications and characterization of mass lesions, but not mass detection in general. Using a higher energy spectrum for digital mammograms while keeping the mean glandular dose constant will lead to a reduction in the image noise and thus to an improved visibility of small microcalcifications, which are in a similar size range as the individual image pixels. Tailored image processing with improved contrast in areas of dense breast parenchyma is probably the main reason behind the improved sensitivity of digital mammography in women with dense breasts. However, processing algorithms that try to equalize the range of signal intensities throughout the breast may also reduce the contrast and with this the visibility of subtle mass lesions.
11.1 Introduction
Ulrich Bick, MD Department of Radiology, CCM, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Detection of breast cancer in digital mammography is based on the same physical principle of X-ray projection imaging as is conventional film-screen mammography. However, differences in image acquisition, processing, and viewing will result in changes in the image appearance, which will affect the visibility of various mammographic signs in different ways (Cole et al. 2005). For a correct diagnosis, it is crucial to get familiar with these differences in image appearance, which may vary between detector type, acquisition
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U. Bick
parameters, and processing algorithms used by a digital mammography or workstation vendor. However, with all discussions regarding differences in image quality between film-screen and digital mammography and between different digital mammography systems, one has to bear in mind that the two main determinants for successful detection of breast cancer in mammography are the skills of the technologist obtaining the images and the experience of the radiologist reading the image. These factors far outweigh any differences in image acquisition technology. This is easily demonstrated by the fact that in the paired screening trials comparing film-screen with digital mammography, around a third of cancers were only found by obtaining a second set of mammographic images independently read by one or more additional radiologists, while differences in cancer detection between digital and film-screen mammography on the whole were almost negligi
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