Dual-source computed tomography (DSCT) uses two source-detector pairs offset by about 90°. In addition to the well-known forward scatter, a special issue in DSCT is cross-scattered radiation from X-ray tube A detected in the detector of system B and vice versa. This effect can lead to artifacts and reduction of the contrast-to-noise ratio of the images. The purpose of this work is to present and evaluate different deep learning-based methods for scatter correction in DSCT.

中文翻译：

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双源CT中基于深度学习的前向和交叉散射校正

双源计算机断层扫描 (DSCT) 使用两个偏移约 90° 的源-检测器对。除了众所周知的前向散射外，DSCT 中的一个特殊问题是系统 B 的探测器中检测到的 X 射线管 A 的交叉散射辐射，反之亦然。这种效应会导致图像的伪影和对比度的降低。这项工作的目的是展示和评估不同的基于深度学习的 DSCT 散射校正方法。