Abstract
Familiarity with common patterns of automated breast US artifacts and their causes can help radiologists reduce preventable false-positive recommendations and increase efficiency.
As automated breast (AB) US becomes more integrated into the daily practice of breast imaging, the need to address the artifacts that interfere with AB US image interpretation is becoming more important. Learning methods to detect and subsequently resolve artifacts such as shadowing can enhance the reader’s confidence and ability to differentiate artifacts and true abnormalities. Understanding the basic principles of AB US and its image acquisition process are key elements in resolving artifacts. Gaining familiarity with the common patterns of AB US artifacts and placing them into categories of technical, software, physiologic, and breast lesion–related causes can aid in image interpretation. Recognizing specific artifacts, such as dropout and lack of contact, and the ability to distinguish them from true abnormalities, such as surgical scars and suspicious lesions, can help minimize preventable false-positive interpretations. Applying methods to confirm shadowing as artifactual, including the use of a second view, additional planes, and software-related tools such as the rotational tool, can aid the radiologist in resolving artifacts and avoiding preventable recalls, potentially resulting in increased specificity. Presented is a methodical approach to recognizing AB US artifacts and their causes; analyzing shadowing, a challenging entity in the interpretation of AB US imaging studies; differentiating artifact from true abnormality; and reviewing characteristic patterns and basic techniques to resolve artifacts. The goal of this article is to enable the radiologist in applying these methods to help reduce preventable false-positive recommendations and increase efficiency in AB US image interpretation.
Online supplemental material is available for this article.
©RSNA, 2019
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Article History
Received: Mar 21 2018Revision requested: June 13 2018
Revision received: Aug 27 2018
Accepted: Aug 30 2018
Published online: May 31 2019
Published in print: July 2019
