Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis.

Abstract

To describe the creation process of a new breast phantom specifically designed to monitor quality control (QC) metrics consistency over several months in digital breast tomosynthesis (DBT). The semi-anthropomorphic Tomomam® phantom was designed and evaluated twice monthly on a single Hologic Selenia Dimensions® unit over 5 months. The phantom is manufactured in a one-piece epoxy resin homogeneous material as the basis for manufacturing, simulating breast tissue as 50% equivalent glandular (GL)/50% equivalent adipose (AD) and compressed thickness of 60mm. The distribution of test objects on different planes inside the phantom should allow the quantification of 10 image quality metrics: reproducibility, signal difference-to-noise ratio (SDNR), geometric distortions in the plane, missing or added tissue at chest wall, at the top and bottom of images stack and lateral sides, in-plane homogeneity, image scoring, artifact spread function (ASF), geometric distortions in the volume. SDNR was quantified according to GL and AD tissues. Tolerance criteria per parameter were described to analyze results over the study time. Mean scores were equal to 15.4, 15.0, and 11.6 for masses, microcalcifications, and fibers, respectively. A large difference between GL and AD tissues for SDNR metrics was noted over the study time: the best results were obtained from GL tissues. Both geometric distortions and local homogeneity in the plane conformed to expected values. The mean volume value of the triangular prism was 11.3% greater than the expected value due to a reconstruction height equal to 66mm instead of 60mm. In this study, we monitored several QC metrics discriminating GL and AD tissues by using a new breast phantom developed by us. The preliminary clinical tests demonstrated that the Tomomam® phantom could be used to reliably and efficiently track 10 QC metrics with a single acquisition. More data need to be acquired to refine tolerance criteria for some metrics.