Forward and cross-scatter estimation in dual source CT using the deep scatter estimation (DSE)

Cross-scatter is often the dominant scatter mode in modern dual source CT (DSCT). Like forward scatter (intra source-detector-pair scatter), which is present in all CT systems, cross-scatter (inter source-detector-pair scatter) leads to streak and cupping artifacts. Having recently developed the deep scatter estimation (DSE) to estimate forward scatter in single source CT, we now tested the performance of DSE in such a cross-scatter-dominated DSCT. Given only the total intensity in a projection as input, we trained a deep convolutional neural network to estimate the scatter distribution, which was then subtracted from the total intensity, to obtain scatter-corrected data. The projections used for training and testing were simulated using Monte Carlo methods. Our method estimates cross- and forward scatter simultaneously and in real-time, with a mean error of only 1.7 %. The error of the CT values is reduced from hundreds of HU to a few dozens of HU. Our method can compete with a measurement-based approach, but does not require any additional hardware.