基于材料分解卷积神经网络的单能CT双能CT成像
Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network
摘要：
双能计算机断层成像（DECT）由于其提供材料特异性信息的巨大潜力，对临床实践具有重要意义。然而，DECT扫描仪通常比标准的单能CT（SECT）扫描仪更昂贵，因此不太适合不发达地区。在本文中，我们证明了标准DECT图像之间的能量域相关性和解剖学一致性可以通过深度学习模型来利用，从而从完全采样的低能量数据以及单视图高能量数据中提供高性能的DECT成像，这些数据可以通过使用侦察视图高能量图像来获得。我们证明了该方法的可行性与对比度增强DECT扫描从22例患者的5,753切片的图像，并显示其优越的性能DECT应用。基于深度学习的方法可能有助于进一步显著降低当前高级DECT扫描仪的辐射剂量，并有可能简化DECT成像系统的硬件，并使用标准SECT扫描仪实现DECT成像。
英文摘要：
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.
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