Scatter correction with deep learning approach for contrast enhanced digital breast tomosynthesis (CEDBT) in both craniocaudal (CC) view and mediolateral oblique (MLO) view

Dual energy contrast-enhanced digital breast tomosynthesis (CEDBT) uses weighted subtraction of two energy spectra to highlight tumor angiogenesis with uptake of iodinated contrast agent. The high energy scan contains more severe scatter radiation than regular low energy DBT. The purpose of this study is to develop a convolutional neural network (CNN) based scatter correction method for dual energy CEDBT in both craniocaudal (CC) view and mediolateral oblique (MLO) view. Anthropomorphic digital breast phantoms with various glandularity and 3D shape were generated using the VICTRE software tool developed by the FDA. The pectoralis muscle layer was inserted into the phantoms for MLO view. Projection images with and without scatter radiation were simulated using Monte Carlo (MC) simulation code of VICTRE, meeting the prototype Siemens Mammomat Inspiration CEDBT system with 300 mu m thick a-Se detector, 25 projections within 46-degree angular range. Scatter radiation ground truth was generated from MC simulated projection images to train CNN. Two separate U-net CNNs were trained to predict scatter radiation maps. Mean absolute percentage error (MAPE) was used as the loss function. The average MAPE of this method is less than 3 % from the ground truth of MC simulation. The proposed scatter correction method was then applied to clinical cases, demonstrating the reduction of cupping artifact and the improvement in contrast object conspicuity.