189 research outputs found
Whole-body PET image denoising for reduced acquisition time
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
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