稀疏视角双能 CT 重建和材料成像的一步逆生成网络 [J].One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

摘要：

目的：稀疏视图双能谱计算机断层扫描（DECT）成像＆＃xD;是一个具有挑战性的反问题。由于收集的数据不完整，条纹伪影的存在会导致重建光谱图像的退化。DECT中的后续材料分解任务可以进一步导致伪影和噪声的放大。
方法：为了解决这个问题，我们＆＃xD;提出了一种新颖的一步逆生成网络（OIGN），用于稀疏视图双＆＃xD;能量CT成像，它可以实现光谱图像和材料的同时成像。整个OIGN由五个子网络组成，形成四个模块，包括预重建模块,预分解模块，以及后续残差过滤模块和残差分解模块。引入残差反馈机制来同步光谱CT 图像和材料的优化。
结果：数值仿真实验表明，与其他最先进的光谱CT成像算法相比，＆＃xD;OIGN在重建和材料分解方面具有更好的性能。OIGN还通过在短短50秒内完成两项高质量的成像任务，展示了高成像效率。此外,进行抗噪声测试以评估OIGN的鲁棒性。意义。这些发现在临床诊断中的高质量多任务能谱＆＃xD;CT成像中具有巨大的潜力。