title: Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study.
abstract: Digital breast tomosynthesis (DBT) is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters (MCCs) for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 at 1.3 false positive clusters, which is below that of current MCC detection algorithms for full-field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal-to-noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.
authors.affiliation: Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA. ireiser@uchicago.edu
authors.forename: I
authors.initials: I
authors.lastname: Reiser
authors.forename: R M
authors.initials: RM
authors.lastname: Nishikawa
authors.forename: A V
authors.initials: AV
authors.lastname: Edwards
authors.forename: D B
authors.initials: DB
authors.lastname: Kopans
authors.forename: R A
authors.initials: RA
authors.lastname: Schmidt
authors.forename: J
authors.initials: J
authors.lastname: Papaioannou
authors.forename: R H
authors.initials: RH
authors.lastname: Moore
mesh_terms: D001185:Artificial Intelligence; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity