Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China, and also with the Department of Radiation Oncology, Stanford University, United States of America.
Department of Radiation Oncology, Stanford University, United States of America.
Phys Med Biol. 2021 Aug 23;66(17). doi: 10.1088/1361-6560/ac195c.
The presence of metallic implants often introduces severe metal artifacts in the x-ray computed tomography (CT) images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (i.e. metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel filtered backward projection (FBP) reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting convolutional neural network (CNN)-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the FBP algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.
金属植入物的存在常常会在 X 射线计算机断层扫描（CT）图像中引入严重的金属伪影，这可能会对临床诊断或放射治疗中的剂量计算产生不利影响。在这项工作中，我们提出了一种基于深度学习的金属伪影减少（MAR）新方法。为了减轻网络学习对解剖学上完全相同的 CT 图像对（即金属伪影污染的 CT 图像和无金属伪影的 CT 图像）的需求，我们提出了一种自监督的跨域学习框架。具体来说，我们训练一个神经网络来恢复给定无金属正弦图中的金属痕迹区域值，其中金属痕迹是通过金属掩模的正向投影来识别的。然后，我们设计了一种新的滤波反向投影（FBP）重建损失，以鼓励网络生成更完美的完成结果，并基于残差学习的图像细化模块来减少重建 CT 图像中的二次伪影。为了保留最终 MAR 图像的精细结构细节和保真度，而不是直接采用卷积神经网络（CNN）细化图像作为输出，我们将金属痕迹替换纳入我们的框架，并使用由 CNN 输出的正向投影生成的先验正弦图替换原始正弦图中受金属影响的投影。然后，我们使用 FBP 算法进行最终的 MAR 图像重建。我们在模拟和真实伪影数据上进行了广泛的评估，以展示我们设计的有效性。我们的方法产生了优越的 MAR 结果，并优于其他有竞争力的方法。我们还展示了我们的框架在其他器官部位的潜力。
