Asymmetric scatter kernel superposition-inspired deep learning approach to estimate scatter in breast tomosynthesis

Digital breast tomosynthesis (DBT) provides pseudo-3D images by acquiring limited angle projections, thus alleviating an inherent limitation of tissue superposition in digital mammography (DM). DBT performance, however, may have limitations in terms of recovery of low-contrast structures and accuracy of material decomposition due to scatter radiation. Employing an anti-scatter grid in DBT can mitigate scatter radiation; however, this would lead to the loss of primary radiation. To compensate for the loss, an increased radiation dose is necessary. Additionally, it requires extra manufacturing costs and adds to the system's complexity. In this work, we propose a deep-learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT. Unlike conventional kernel-based methods which estimate the scatter field based on the value of an individual pixel, the proposed method generates the scatter amplitude and width maps through a network. Additionally, the asymmetric factor map is also estimated from the network to accommodate local variations in conjunction with the object thickness and shape variation. Experiments demonstrate the superiority of the proposed approach. We believe the clinical impact of the proposed method is high since it can negate the additional radiation dose and the system complexity associated with integrating an anti-scatter grid in the DBT system.