Background and objectives

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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一种新颖的监督学习方法，可生成CT图像以进行延迟宠物扫描中的衰减校正

背景和目标

衰减校正对于PET图像重建很重要。在临床PET / CT扫描中，衰减信息通常是通过CT获得的。但是，额外的CT扫描延迟PET成像可能会增加患癌症的风险。在本文中，我们提出了一种新颖的CT生成方法，用于延迟PET成像中的衰减校正，不需要额外的CT扫描。

方法

由于只有PET原始数据可用于延迟PET扫描，因此常规图像配准方法很难直接使用。为了解决该问题，开发了一种重建网络以首先从原始数据生成伪PET图像。然后，使用第二个网络通过将PET / CT图像从第一次扫描映射到延迟扫描来生成CT图像。第二个网络的输入是来自第一次扫描和延迟扫描的两个伪PET图像，以及来自第一次扫描的CT图像。这些标签是从延迟扫描中的地面真实CT图像中获取的。损失函数包含一个图像相似项和一个正则项，它们分别反映了解剖结构的匹配精度和非刚性变形场的平滑度。

结果

我们用模拟和临床PET / CT数据集评估了提出的方法。计算标准摄取值，并将其与黄金标准（通过共同注册的CT进行衰减校正）进行比较。结果表明，提出的监督学习方法可以生成高质量，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MAE和RMSE分别为4.61和22.75，重建的PET图像和地面真实PET图像之间的平均PSNR为62.13 dB。

结论

所提出的方法能够生成准确的CT图像，用于延迟PET扫描中的衰减校正。实验表明，该方法在定量PET图像准确性方面优于传统方法。