Ma Genwei, Zhao Xing, Zhu Yining, Zhang Huitao
School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China.
Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, People's Republic of China.
Phys Med Biol. 2022 Feb 1;67(3). doi: 10.1088/1361-6560/ac4122.
Several reconstruction networks have been invented to solve the problem of learning-based computed tomography (CT) reconstruction. However, the application of neural networks to tomographic reconstruction remains challenging due to unacceptable memory space requirements. In this study, we present a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which respectively correspond to the filter and back-projection of the FBP method. The first module of the LBRN decouples the relationship of the Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection, can use the block reconstruction strategy. Because each image block is only connected with part-filtered projection data, the network structure is greatly simplified and the parameters of the whole network are dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data, and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest, metal artifact reduction and a real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.
为了解决基于学习的计算机断层扫描（CT）重建问题，人们发明了几种重建网络。然而，由于难以接受的内存空间需求，将神经网络应用于断层扫描重建仍然具有挑战性。在本研究中，我们提出了一种新颖的轻量级块重建网络（LBRN），它通过展开滤波反投影（FBP）方法将重建算子转换为深度神经网络。具体而言，所提出的网络包含两个主要模块，分别对应于FBP方法的滤波和反投影。LBRN的第一个模块解耦了重建图像与投影数据之间的拉东变换关系。因此，后续模块，即块反投影，可以使用块重建策略。由于每个图像块仅与部分滤波后的投影数据相连，网络结构大大简化，整个网络的参数也大幅减少。此外，该方法是端到端训练的，直接从原始投影数据开始工作，并且不依赖于任何初始图像。进行了五个重建实验来评估所提出的LBRN的性能：全角度、低剂量CT、感兴趣区域、金属伪影减少和一个真实数据实验。实验结果表明，LBRN可以有效地引入重建过程，并且在不同的重建问题方面具有突出优势。
