Li Siqi, Wang Guobao
Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, United States.
Proc SPIE Int Soc Opt Eng. 2021 Feb;11595. doi: 10.1117/12.2582317. Epub 2021 Feb 15.
The PET-enabled dual-energy CT method allows dual-energy CT imaging on PET/CT scanners without the need for a second x-ray CT scan. A 511 keV γ-ray attenuation image can be reconstructed from time-of-flight PET emission data using the maximum-likelihood attenuation and activity (MLAA) algorithm. However, the attenuation image reconstructed by standard MLAA is commonly noisy. To suppress noise, we propose a neural-network approach for MLAA reconstruction. The PET attenuation image is described as a function of kernelized neural networks with the flexibility of incorporating the available x-ray CT image as anatomical prior. By applying optimization transfer, the complex optimization problem of the proposed neural MLAA reconstruction is solved by a modularized iterative algorithm with each iteration decomposed into three steps: PET activity image update, attenuation image update, and neural-network learning using a weighed mean squared-error loss. The optimization algorithm is guaranteed to monotonically increase the data likelihood. The results from computer simulations demonstrated the neural MLAA algorithm can achieve a significant improvement on the γ-ray CT image quality as compared to other algorithms.
PET 辅助双能 CT 方法可在 PET/CT 扫描仪上进行双能 CT 成像，无需进行第二次 X 射线 CT 扫描。利用最大似然衰减与活度（MLAA）算法，可从飞行时间 PET 发射数据重建出 511 keV γ 射线衰减图像。然而，通过标准 MLAA 重建的衰减图像通常存在噪声。为了抑制噪声，我们提出了一种用于 MLAA 重建的神经网络方法。PET 衰减图像被描述为核神经网络的函数，具有将可用的 X 射线 CT 图像作为解剖学先验信息纳入的灵活性。通过应用优化传递，所提出的神经 MLAA 重建的复杂优化问题通过模块化迭代算法解决，每次迭代分解为三个步骤：PET 活度图像更新、衰减图像更新以及使用加权均方误差损失的神经网络学习。该优化算法保证能单调增加数据似然性。计算机模拟结果表明，与其他算法相比，神经 MLAA 算法可显著提高 γ 射线 CT 图像质量。
