  Positron Emission Tomography (PET) is an essential technique in many clinical
applications that allows for quantitative imaging at the molecular level. This
study aims to develop a denoising method using novel dilated convolutional
neural network to recover full-count images from low-count images. We adopted
similar hierarchal structure from the conventional uNet and incorporated
dilated kernels in each convolution to allow the network to observe larger, and
perhaps, more robust, features within the image. Our dNet were trained
alongside a uNet for comparison. Our 2.5D model used a training set (N=30) and
testing set (N=5) that were obtained from an ongoing 18F-FDG study. Low-count
PET data (10% count) were generated through Poisson thinning from the full
listmode file. Both low-count PET and full-count PET were reconstructed with
the OSEM algorithm. Objective imaging metrics including mean absolute percent
error (MAPE), peak signal-to-noise ratio (PSNR) and structural similarity index
metric (SSIM) were used to analyze the denoising methods. Both the uNet and our
proposed dNet were successfully trained to synthesize full-count PET images
from low-count PET images. Compared to low-count PET, both the uNet and dNet
methods significantly improved MAPE, PSNR and SSIM. Our dNet also
systematically outperformed uNet on all three metrics and across all testing
subjects. This study proposed a novel approach of using dilated convolutions
for recovering full-count PET images from low-count PET images. Our dNet
significantly outperformed the well-established uNet and demonstrates great
potential for denoising low-count PET images.
