Abstract
Digital breast tomosynthesis (DBT) reduces breast tissue overlap, which is a major limitation of digital mammography. However, DBT does not show significant improvement in calcification detection, because of the limited angle and small number of projections used to reconstruct the 3D breast volume. Virtual clinical trials (VCTs) were used to evaluate the benefits of computed super resolution (SR) and the optimal combination of the acquisition parameters to improve calcification detection in DBT. We simulated calcifications that were embedded into software breast phantoms. DBT projections of the breast phantoms with and without calcifications were synthesized. We simulated detector elements of 0.085 mm and reconstructed DBT images using 0.0425 mm and 0.085 mm voxels. Channelized Hotelling observers (CHOs) were trained and tested to simulate five virtual readers. Differences in area under the curve (AUC) between SR images and images synthesized with 0.085 mm voxels were calculated using the one-shot multiple-reader multiple-case receiver operator curve (MRMC ROC) methods. Our results show that the differences in AUC is approximately 0.10, 0.03 and 0.03 for DBT images simulated using calcifications sizes 0.001 mm(3), 0.002 mm(3), and 0.003 mm(3), respectively. SR shows a substantial improvement for calcification detection in DBT. The impact of SR on calcification detection is more prominent for small calcifications.