  In PET, the amount of relative (signal-dependent) noise present in different
body regions can be significantly different and is inherently related to the
number of counts present in that region. The number of counts in a region
depends, in principle and among other factors, on the total administered
activity, scanner sensitivity, image acquisition duration, radiopharmaceutical
tracer uptake in the region, and patient local body morphometry surrounding the
region. In theory, less amount of denoising operations is needed to denoise a
high-count (low relative noise) image than images a low-count (high relative
noise) image, and vice versa. The current deep-learning-based methods for PET
image denoising are predominantly trained on image appearance only and have no
special treatment for images of different noise levels. Our hypothesis is that
by explicitly providing the local relative noise level of the input image to a
deep convolutional neural network (DCNN), the DCNN can outperform itself
trained on image appearance only. To this end, we propose a noise-level-aware
framework denoising framework that allows embedding of local noise level into a
DCNN. The proposed is trained and tested on 30 and 15 patient PET images
acquired on a GE Discovery MI PET/CT system. Our experiments showed that the
increases in both PSNR and SSIM from our backbone network with relative noise
level embedding (NLE) versus the same network without NLE were statistically
significant with p<0.001, and the proposed method significantly outperformed a
strong baseline method by a large margin.
