This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times. Our experiments consider 212 studies (56908 images), and evaluate the models using 2D (RMSE, SSIM) and 3D (SUVpeak and SUVmax error for the regions of interest) metrics. It was shown that, in contrast to previous studies, supervised models (ResNet, Unet, SwinIR) outperform unsupervised models (pix2pix GAN and CycleGAN with ResNet backbone and various auxiliary losses) in the reconstruction of 2D PET images. Moreover, a hybrid approach of supervised CycleGAN shows the best results in SUVmax estimation for denoised images, and the SUVmax estimation error for denoised images is comparable with the PET reproducibility error.
翻译：本文评估了有监督和无监督深度学习模型在减少采集时间的情况下去噪正电子发射断层扫描（PET）图像的性能。我们的实验考虑了 212 项研究（56908 张图像），并使用 2D（RMSE、SSIM）和 3D（针对感兴趣区域的 SUVpeak 和 SUVmax），这些指标评估了模型的性能。结果表明，与以前的研究不同，监督模型（ResNet、Unet、SwinIR）在重建 2D PET 图像方面的表现优于无监督模型（pix2pix GAN 和带有 ResNet 骨干和各种辅助损失的 CycleGAN）。此外，监督的 CycleGAN 混合方法在去噪图像的 SUVmax 估计中表现最佳，去噪图像的 SUVmax 估计误差与 PET 重复性误差相当。
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