A novel supervised learning method to generate CT images for attenuation correction in delayed pet scans. (December 2020)

• Record Type: Journal Article
• Authors: Rao, Fan; Yang, Bao; Chen, Yen-Wei; Li, Jingsong; Wang, Hongkai; Ye, Hongwei; Wang, Yaofa; Zhao, Kui; Zhu, Wentao
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Attenuation correction -- Non-rigid image registration -- Delayed PET scan -- Supervised learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105764
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A reconstruction network is developed to convert PET raw data into pseudo PET image. An elaborate network is designed to generate CT image for attenuation correction in PET image reconstruction. Inputs of the network are the two pseudo PET images in the first and delayed scan, and the CT image in the first scan. The network can output an estimated CT image in the delayed scan. This method only requires one CT scan and therefore significantly reduces the X-ray radiation dose received by the patient in delayed PET imaging. Abstract: 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.