论文标题
使用自动编码器用于宠物双能CT的修改内核MLAA
Modified Kernel MLAA Using Autoencoder for PET-enabled Dual-Energy CT
论文作者
论文摘要
PET和双能CT的合并使用提供了用于多参数成像的互补信息。 PetenableD双能CT结合了低能量X射线CT图像与高能量和γ＆-Ray CT（GCT）图像从飞行时间PET发射数据重建，以启用对PET/CT扫描仪上的双能量CT材料分解。最大液化性衰减和活动（MLAA）算法已用于GCT重建，但患有噪声。内核MLAA在通过内核框架之前利用了X射线CT图像，以指导GCT重建，并证明了噪声抑制作用的重大改善。但是，类似于图像重建的其他内核方法，现有的内核MLAA使用基于图像强度的特征来构建内核表示，这并不总是强大的，并且可能导致具有文物的次优重建。在本文中，我们通过使用自动编码器卷积神经网络（CNN）提出一种修改的内核方法来提取从X射线CT图像之前提取固有特征。进行了一项计算机模拟研究，以将自动编码器CNN衍生的特征表示与原始图像贴片进行比较，以评估用于GCT图像重建和双能量多材料分解的内核MLAA。结果表明，与现有核MLAA算法相比，自动编码器内核MLAA方法可以对GCT和材料分解实现显着的图像质量改进。该方法的一个弱点是它在骨骼区域的潜在过度平滑性，表明在未来的工作中进一步优化的重要性。这些代码可在https://github.com/siqili1020/autoencoder- kernel-mlaa上找到。
Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PETenabled dual-energy CT combines a low-energy x-ray CT image with a high-energy &γ&-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 maximumlikelihood 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 artifacts. 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 multimaterial 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. The codes is available on https://github.com/SiqiLi1020/Autoencoder- Kernel-MLAA.
