  Although supervised convolutional neural networks (CNNs) often outperform
conventional alternatives for denoising positron emission tomography (PET)
images, they require many low- and high-quality reference PET image pairs.
Herein, we propose an unsupervised 3D PET image denoising method based on an
anatomical information-guided attention mechanism. The proposed magnetic
resonance-guided deep decoder (MR-GDD) utilizes the spatial details and
semantic features of MR-guidance image more effectively by introducing
encoder-decoder and deep decoder subnetworks. Moreover, the specific shapes and
patterns of the guidance image do not affect the denoised PET image, because
the guidance image is input to the network through an attention gate. In a
Monte Carlo simulation of [$^{18}$F]fluoro-2-deoxy-D-glucose (FDG), the
proposed method achieved the highest peak signal-to-noise ratio and structural
similarity (27.92 $\pm$ 0.44 dB/0.886 $\pm$ 0.007), as compared with Gaussian
filtering (26.68 $\pm$ 0.10 dB/0.807 $\pm$ 0.004), image guided filtering
(27.40 $\pm$ 0.11 dB/0.849 $\pm$ 0.003), deep image prior (DIP) (24.22 $\pm$
0.43 dB/0.737 $\pm$ 0.017), and MR-DIP (27.65 $\pm$ 0.42 dB/0.879 $\pm$ 0.007).
Furthermore, we experimentally visualized the behavior of the optimization
process, which is often unknown in unsupervised CNN-based restoration problems.
For preclinical (using [$^{18}$F]FDG and [$^{11}$C]raclopride) and clinical
(using [$^{18}$F]florbetapir) studies, the proposed method demonstrates
state-of-the-art denoising performance while retaining spatial resolution and
quantitative accuracy, despite using a common network architecture for various
noisy PET images with 1/10th of the full counts. These results suggest that the
proposed MR-GDD can reduce PET scan times and PET tracer doses considerably
without impacting patients.
