A deep learning approach for 18F-FDG PET attenuation correction

BackgroundTo develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously valued pseudo-computed tomography (CT) images from uncorrected F-18-fluorodeoxyglucose (F-18-FDG) PET images. A deep convolutional encoder-decoder network was trained to identify tissue contrast in volumetric uncorrected PET images co-registered to CT data. A set of 100 retrospective 3D FDG PET head images was used to train the model. The model was evaluated in another 28 patients by comparing the generated pseudo-CT to the acquired CT using Dice coefficient and mean absolute error (MAE) and finally by comparing reconstructed PET images using the pseudo-CT and acquired CT for attenuation correction. Paired-sample t tests were used for statistical analysis to compare PET reconstruction error using deepAC with CT-based attenuation correction.ResultsdeepAC produced pseudo-CTs with Dice coefficients of 0.800.02 for air, 0.94 +/- 0.01 for soft tissue, and 0.75 +/- 0.03 for bone and MAE of 111 +/- 16 HU relative to the PET/CT dataset. deepAC provides quantitatively accurate F-18-FDG PET results with average errors of less than 1% in most brain regions.Conclusions We have developed an automated approach (deepAC) that allows generation of a continuously valued pseudo-CT from a single F-18-FDG non-attenuation-corrected (NAC) PET image and evaluated it in PET/CT brain imaging.