Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

Objective To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. Methods Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. Results Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.23.65 and 0.9890.006 for the external validation set, respectively. RE (%) of SUVmean was -0.102.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, -0.83 to 1.18). SUVmax had mean RE (%) of -3.872.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of -3.99 +/- 2.11 (range, -8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 +/- 2.99. Conclusions Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners.