论文标题
重量编码重建网络，用于以半案例和学习方式的计算机断层扫描
Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way
论文作者
论文摘要
计算机断层扫描的经典代数重建技术（ART）需要预定的体素重量来投影像素值。但是，由于物理理解和计算资源的限制，无法准确获得这样的权重。在这项研究中，我们提出了一种名为“权重编码重建网络（WERNET）”的基于半案例学习的方法，以解决上述问题。该模型以自我监督的方式进行训练，没有体素集的标签。它包含两个分支，包括体素重量编码器和体素注意部分。使用梯度归一化，我们能够在数值上稳定地共同培训编码器和体素集。使用Wernet，与地面真相大于0.999的余弦相似性获得了重建的结果。此外，该模型显示了与经典艺术方法相比的非凡能力。在模型的概括测试中，编码器可从具有复杂结构的体素集中转移到看不见的情况，而无需扣除精度。
Classic algebraic reconstruction technology (ART) for computed tomography requires pre-determined weights of the voxels for projecting pixel values. However, such weight cannot be accurately obtained due to the limitation of the physical understanding and computation resources. In this study, we propose a semi-case-wise learning-based method named Weight Encode Reconstruction Network (WERNet) to tackle the issues mentioned above. The model is trained in a self-supervised manner without the label of a voxel set. It contains two branches, including the voxel weight encoder and the voxel attention part. Using gradient normalization, we are able to co-train the encoder and voxel set numerically stably. With WERNet, the reconstructed result was obtained with a cosine similarity greater than 0.999 with the ground truth. Moreover, the model shows the extraordinary capability of denoising comparing to the classic ART method. In the generalization test of the model, the encoder is transferable from a voxel set with complex structure to the unseen cases without the deduction of the accuracy.
