TomoGAN：低剂量同步辐射X射线断层扫描与生成对抗网络
TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks
摘要：
基于同步辐射的X射线断层扫描是一种非侵入性成像技术，可以以从几十微米到几纳米的高空间分辨率重建材料的内部结构。然而，为了在较小的长度尺度上分辨样品特征，需要较高的辐射剂量。因此，对可实现的分辨率的限制主要由这些长度尺度上的噪声来设置。我们提出了\TOMOGAN{}，一种基于生成对抗网络的去噪技术，用于提高低剂量成像条件下重建图像的质量。我们在两个光子预算有限的实验条件下评估我们的方法：（1）足够数量的低剂量投影（基于奈奎斯特采样），以及（2）不足或有限数量的高剂量投影。在这两种情况下，角采样被假定为各向同性的，并且整个实验中的光子预算基于样品上的最大允许辐射剂量是固定的。模拟和实验数据集的评估表明，我们的方法可以显着降低重建图像中的噪声，提高模拟和实验数据的结构相似性得分从0.18到0.9和从0.18到0.41，分别。此外，滤波反投影重建图像的质量，其次是我们的去噪方法超过了重建的同时迭代重建技术，显示了我们的方法的计算优势。
英文摘要：
Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.
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