방사선의학 웹진

바로가기  >

Abstract
Purpose: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET.

Methods: One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNNNAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing.

Results: The error of the attenuation correction factors estimated using μ-CT and μ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%).

Conclusion: The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.

Affiliations

Donghwi Hwang  1   2   3 , Seung Kwan Kang  1   2   3   4 , Kyeong Yun Kim  1   2   4 , Hongyoon Choi  2 , Jae Sung Lee  5   6   7   8   9
1 Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
2 Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
3 Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
4 Brightonix Imaging Inc., Seoul, South Korea.
5 Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea. jaes@snu.ac.kr.
6 Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. jaes@snu.ac.kr.
7 Artificial Intelligence Institute, Seoul National University, Seoul, South Korea. jaes@snu.ac.kr.
8 Brightonix Imaging Inc., Seoul, South Korea. jaes@snu.ac.kr.
9 Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea. jaes@snu.ac.kr.