University of California Davis Medical Center, Department of Radiology, Saramento, CA, USA.
Philos Trans A Math Phys Eng Sci. 2021 Aug 23;379(2204):20200204. doi: 10.1098/rsta.2020.0204. Epub 2021 Jul 5.
Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy -ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
正电子发射断层扫描（PET）与双能 CT 的联合使用可为多参数成像提供互补信息。基于 PET 的双能 CT 将低能 X 射线 CT 图像与基于飞行时间 PET 发射数据重建的高能射线 CT（GCT）图像相结合，从而使 PET/CT 扫描仪能够进行双能 CT 物质分解。最大似然衰减和活动（MLAA）算法已被用于 GCT 重建，但存在噪声问题。核 MLAA 通过核框架利用 X 射线 CT 图像先验来指导 GCT 重建，并已证明在抑制噪声方面有显著的改进。然而，与其他用于图像重建的核方法类似，现有的核 MLAA 使用基于图像强度的特征来构建核表示，这并不总是稳健的，并且可能导致具有伪影的次优重建。在本文中，我们提出了一种改进的核方法，通过使用自动编码器卷积神经网络（CNN）从 X 射线 CT 图像先验中提取内在特征集。进行了计算机模拟研究，以比较自动编码器 CNN 衍生的特征表示与原始图像块，用于评估 GCT 图像重建和双能多物质分解的核 MLAA。结果表明，与现有的核 MLAA 算法相比，自动编码器核 MLAA 方法可以显著提高 GCT 和物质分解的图像质量。该方法的一个弱点是在骨区域可能存在过度平滑，这表明在未来的工作中需要进一步优化。本文是主题为“协同层析图像重建：第 2 部分”的一部分。
