论文标题
来自单能CT数据的双能量CT成像，具有材料分解卷积神经网络
Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network
论文作者
论文摘要
双能计算机断层扫描（DECT）对临床实践具有重要意义，因为它提供了特定于材料的信息的巨大潜力。但是，DECT扫描仪通常比标准单能CT（SECT）扫描仪更昂贵，因此未开发区域易于使用。在本文中，我们表明，可以通过深度学习模型来利用标准DECT图像之间的能量域相关性和解剖一致性，从而从完全采样的低能数据以及单视图高能数据以及可以通过使用搜索搜索搜索搜索搜索搜索高效率的高能图像来提供高性能的DECT成像。我们通过5,753片22名患者的图像进行了对比增强的DECT扫描，该方法的可行性，并在DECT应用上表现出了出色的性能。基于深度学习的方法对于进一步显着减少当前高级DECT DECT扫描仪的辐射剂量可能是有用的，并且有可能简化DECT成像系统的硬件，并使用标准的SECT扫描仪启用DECT成像。
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data, which can be obtained by using a scout-view high-energy image. We demonstrate the feasibility of the approach with contrast-enhanced DECT scans from 5,753 slices of images of twenty-two patients and show its superior performance on DECT applications. The deep learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.
