Dynamic contrast breast MRI is becoming an important adjunct in screening women at high risk for breast cancer,
determining extent of disease (staging) and monitoring response to therapy. In dynamic contrast breast MRI,
regions of rapid contrast uptake indicate increases in vascularity which can be associated with abnormal tissue,
sometimes significant for malignant disease. To show these areas of enhancement, subtractions between the pre and
post contrast images and maximum intensity projections (MIPs) are computed. Many projections are obscured by
normally enhancing anatomy (heart, aorta, pulmonary vessels). Identification of these structures allows their
removal from MIPs, which improves image quality, diagnostic utility and the conspicuity of the enhancing regions.
In this study, a fully automated classifier is presented which uses the spatial location of enhancing regions to
separate those that occur inside the chest wall from those occurring in the tissue of interest (breast, axilla, chest
wall). The classifier was trained on 21 studies each acquired at a different institution (699 clusters of pixels), and
tested on 7 studies (231 clusters of pixels) that were not part of the training set. Multiple cost functions for training
were examined. The measurements for the peak performance of the classifier were sensitivity 97.0%, specificity
99.4%, PPV 99.9%, NPV 78.8%.