Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography

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 -CNN NAC ). 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 -CNN MLAA ). We investigated the improvement in the CNN performance by combining the two methods ( μ -CNN MLAA+NAC ) and the suitability of μ -CNN NAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18 F-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 -CNN NAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ -CNN NAC . However, CNN NAC provided less accurate bone structures in the μ -maps, while the best results in recovering the fine bone structures were obtained by applying CNN MLAA+NAC . Additionally, the μ -values in the lungs were overestimated by CNN NAC . Activity images ( λ ) corrected for attenuation using μ -CNN MLAA and μ -CNN MLAA+NAC were superior to those corrected using μ -CNN NAC , in terms of their similarity to λ -CT. However, the improvement in the similarity with λ -CT by combining the CNN NAC and CNN MLAA approaches was insignificant (percent error for lung cancer lesions, λ -CNN NAC = 5.45% ± 7.88%; λ -CNN MLAA = 1.21% ± 5.74%; λ -CNN MLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ -CNN NAC = 1.37% ± 5.16%; λ -CNN MLAA = 0.23% ± 3.81%; λ -CNN MLAA+NAC = 0.05% ± 3.49%). Conclusion The use of CNN NAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNN MLAA outperformed CNN NAC.