In automated manufacturing and safety inspection, there is a high demand for fast computed tomography (CT) scanning and image reconstruction. Currently, faster scanning can be achieved by reducing the X-ray exposure time within sparse view CT. The faster scanning strategy introduces significant streak artefacts and noise during the sampling process. Consequently, streak artefacts and noise need to be simultaneously suppressed, which is poses a challenge for existing reconstruction methods. This paper presents a fast iterative reconstruction algorithm that can simultaneously suppress both streak artefacts and noise. This method can not only reconstruct high-fidelity images from rapidly acquired projection data, but also has a faster reconstruction speed than the existing iterative reconstruction algorithms. First, we present a high-order multi-directional total variation (HOM-TV) method that specifically focuses on preserving edge details of the image. Then, we present a fast iterative reconstruction model by incorporating HOM-TV and non-local means into the objective function. Finally, the effectiveness of the presented reconstruction model is validated by simulation and real experiments. The faster scanning method can complete the scan in only 5 seconds, and the structural similarity index (SSIM) of the CT image reconstructed by our method is 0.9755, which is higher than 0.0175 of the Fast Null Space Reconstruction (FNSR) algorithm. The peak signal-to-noise ratio (PSNR) index is 1.656, which is higher than that of the contrast algorithm. In terms of reconstruction time, our algorithm can achieve reconstruction in as little as 36 seconds, outperforming the baseline algorithms.

中文翻译：

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通过工业CT快速扫描重建CT图像

在自动化制造和安全检查中，对快速计算机断层扫描（CT）扫描和图像重建有很高的需求。目前，可以通过减少稀疏视图 CT 内的 X 射线曝光时间来实现更快的扫描。更快的扫描策略在采样过程中引入了显着的条纹伪影和噪声。因此，需要同时抑制条纹伪影和噪声，这对现有的重建方法提出了挑战。本文提出了一种快速迭代重建算法，可以同时抑制条纹伪影和噪声。该方法不仅可以从快速获取的投影数据中重建高保真图像，而且比现有的迭代重建算法具有更快的重建速度。首先，我们提出了一种高阶多向全变分（HOM-TV）方法，该方法专门致力于保留图像的边缘细节。然后，我们通过将 HOM-TV 和非局部方法结合到目标函数中，提出了一种快速迭代重建模型。最后，通过仿真和实际实验验证了所提出的重建模型的有效性。更快的扫描方法只需5秒即可完成扫描，并且我们的方法重建的CT图像的结构相似指数（SSIM）为0.9755，高于快速零空间重建（FNSR）算法的0.0175。峰值信噪比（PSNR）指数为1.656，高于对比算法。在重建时间方面，我们的算法可以在短短36秒内实现重建，优于基线算法。