Abstract：
BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefact removal.
OBJECTIVE: Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for training data.
METHODS: We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging of patients.
RESULTS: The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images.
CONCLUSIONS: Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinical radiology.
OBJECTIVE: Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for training data.
METHODS: We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging of patients.
RESULTS: The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images.
CONCLUSIONS: Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinical radiology.
摘要：
背景：计算机断层扫描（CT）依赖于X射线的衰减，而且是，因此，对身体弱衰减器官的有限使用，比如肺。X射线暗场(DF)成像是最近开发的技术,其利用X射线光栅来实现小角度散射作为替代的对比度机制。DF信号提供有关物体微观形态的结构信息，补充传统的衰减信号。我们小组开发了第一个人体规模的X射线DFCT。尽管有专门的处理算法，重建的图像仍然受到条纹伪影的影响，这往往会阻碍图像的解释。近年来,卷积神经网络在CT重建领域得到了广泛的应用,除其他外，用于清除文物。
目的：减少条纹伪影对于优化DFCT的图像质量至关重要，和无伪影图像是潜在的未来临床应用的先决条件。本文的目的是证明CNN后处理在X射线DFCT中减少伪影的可行性，以及多旋转扫描如何作为训练数据的途径。
方法：我们采用了有监督的深度学习方法，使用三维双框架UNet来去除条纹伪影。所需的训练数据是从我们研究所的实验X射线DFCT原型获得的。使用两种不同的操作模式来生成输入和相应的地面实况数据集。剂量相容辐射水平的临床相关扫描被用作输入数据，和具有更少伪影的扩展扫描被用作地面实况数据。后者既不是剂量-，也不兼容时间，因此,对患者的临床成像不可行。
结果：经过训练的CNN能够大大减少DFCT图像中的条纹伪影。对网络进行了完全不同的图像测试，以前看不见的图像特征。在所有情况下,CNN处理大大提高了图像质量，这通过增加的图像质量指标定量证实。在加工过程中保留了精细的细节，尽管输出图像看起来比地面实况图像更平滑。
结论：我们的结果展示了神经网络减少X射线DFCT中条纹伪影的潜力。在剂量兼容的X射线DFCT中成功增强了图像质量，这对于X射线DFCT在现代临床放射学中的应用起着至关重要的作用。
目的：减少条纹伪影对于优化DFCT的图像质量至关重要，和无伪影图像是潜在的未来临床应用的先决条件。本文的目的是证明CNN后处理在X射线DFCT中减少伪影的可行性，以及多旋转扫描如何作为训练数据的途径。
方法：我们采用了有监督的深度学习方法，使用三维双框架UNet来去除条纹伪影。所需的训练数据是从我们研究所的实验X射线DFCT原型获得的。使用两种不同的操作模式来生成输入和相应的地面实况数据集。剂量相容辐射水平的临床相关扫描被用作输入数据，和具有更少伪影的扩展扫描被用作地面实况数据。后者既不是剂量-，也不兼容时间，因此,对患者的临床成像不可行。
结果：经过训练的CNN能够大大减少DFCT图像中的条纹伪影。对网络进行了完全不同的图像测试，以前看不见的图像特征。在所有情况下,CNN处理大大提高了图像质量，这通过增加的图像质量指标定量证实。在加工过程中保留了精细的细节，尽管输出图像看起来比地面实况图像更平滑。
结论：我们的结果展示了神经网络减少X射线DFCT中条纹伪影的潜力。在剂量兼容的X射线DFCT中成功增强了图像质量，这对于X射线DFCT在现代临床放射学中的应用起着至关重要的作用。
