Rao Fan, Yang Bao, Chen Yen-Wei, Li Jingsong, Wang Hongkai, Ye Hongwei, Wang Yaofa, Zhao Kui, Zhu Wentao
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
School of Biomedical Engineering, Dalian University of Technology, Dalian, China.
Comput Methods Programs Biomed. 2020 Dec;197:105764. doi: 10.1016/j.cmpb.2020.105764. Epub 2020 Sep 30.
Attenuation correction is important for PET image reconstruction. In clinical PET/CT scans, the attenuation information is usually obtained by CT. However, additional CT scans for delayed PET imaging may increase the risk of cancer. In this paper, we propose a novel CT generation method for attenuation correction in delayed PET imaging that requires no additional CT scans.
As only PET raw data is available for the delayed PET scan, routine image registration methods are difficult to use directly. To solve this problem, a reconstruction network is developed to produce pseudo PET images from raw data first. Then a second network is used to generate the CT image through mapping PET/CT images from the first scan to the delayed scan. The inputs of the second network are the two pseudo PET images from the first and delayed scans, and the CT image from the first scan. The labels are taken from the ground truth CT image in the delayed scan. The loss function contains an image similarity term and a regularization term, which reflect the anatomy matching accuracy and the smoothness of the non-rigid deformation field, respectively.
We evaluated the proposed method with simulated and clinical PET/CT datasets. Standard Uptake Value was computed and compared with the gold standard (with coregistered CT for attenuation correction). The results show that the proposed supervised learning method can generate PET images with high quality and quantitative accuracy. For the test cases in our study, the average MAE and RMSE of the proposed supervised learning method were 4.61 and 22.75 respectively, and the average PSNR between the reconstructed PET image and the ground truth PET image was 62.13 dB.
The proposed method is able to generate accurate CT images for attenuation correction in delayed PET scans. Experiments indicate that the proposed method outperforms traditional methods with respect to quantitative PET image accuracy.
衰减校正在正电子发射断层扫描（PET）图像重建中至关重要。在临床PET/CT扫描中，衰减信息通常通过CT获取。然而，为延迟PET成像进行额外的CT扫描可能会增加癌症风险。在本文中，我们提出了一种用于延迟PET成像中衰减校正的新型CT生成方法，该方法无需额外的CT扫描。
由于延迟PET扫描仅可获得PET原始数据，常规图像配准方法难以直接使用。为解决此问题，首先开发了一个重建网络，用于从原始数据生成伪PET图像。然后使用第二个网络通过将第一次扫描的PET/CT图像映射到延迟扫描来生成CT图像。第二个网络的输入是第一次扫描和延迟扫描的两个伪PET图像以及第一次扫描的CT图像。标签取自延迟扫描中的真实CT图像。损失函数包含一个图像相似性项和一个正则化项，分别反映解剖结构匹配精度和非刚性变形场的平滑度。
我们使用模拟和临床PET/CT数据集评估了所提出的方法。计算了标准摄取值并与金标准（使用配准的CT进行衰减校正）进行比较。结果表明，所提出的监督学习方法可以生成高质量和定量准确的PET图像。对于我们研究中的测试案例，所提出的监督学习方法的平均平均绝对误差（MAE）和均方根误差（RMSE）分别为4.61和22.75，重建的PET图像与真实PET图像之间的平均峰值信噪比（PSNR）为62.13dB。
所提出的方法能够为延迟PET扫描中的衰减校正生成准确的CT图像。实验表明，在所提出的方法在定量PET图像准确性方面优于传统方法。
