Whole-body PET image denoising for reduced acquisition time
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 71 references that are in the map. First posted on arXiv 28 Mar 2023.
Builds on
17 of 71 references.
References · 17
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5 more
- 2021Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancementcited by 6
- 2022Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural networkcited by 4
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- 2022Quasi-supervised Learning for Super-resolution PETcited by 3
- 2022Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?cited by 1
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Citing papers per year: 2026: 1.
1 paper.