Using virtual digital breast tomosynthesis for de-noising of low-dose projection images

Digital Breast Tomosynthesis (DBT) provides a quasi-3D impression of the breast volume resulting in a better visualization of mass. However, one serious drawback of Tomosynthesis is that compared to Mammography, each projection gets lower x-ray dose resulting into higher quantum noise which seriously hampers the visibility of calcifications. To solve this problem we propose a Convolutional Neural Network model based on Adversarial loss. We train the deep network using synthetic data obtained from Virtual Clinical Trials. Unlike earlier works which tested model on phantoms only, we performed experiments on real samples obtained in clinical settings as well. Our approach shows encouraging results in de-noising the projections. De-noised projections show higher perceptual similarity with mammograms and superior signal-to-noise ratio. The reconstructed volume also enhances calcification visibility. Our work shows the viability of utilizing synthetic data for training the deep network for de-noising purposes.