# Whole-body PET image denoising for reduced acquisition time

arXiv: https://arxiv.org/abs/2303.16085 · HTML: https://mlsanity.com/paper/2303.16085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 71 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2209.15076/) (2022)
- [PET image denoising based on denoising diffusion probabilistic models](https://mlsanity.com/paper/2209.06167/) (2022)
- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising](https://mlsanity.com/paper/2209.03300/) (2022)
- [Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising](https://mlsanity.com/paper/2104.08538/) (2021)
- [Full-count PET Recovery from Low-count Image Using a Dilated Convolutional Neural Network](https://mlsanity.com/paper/1910.11865/) (2019)
- [TransEM:Residual Swin-Transformer based regularized PET image reconstruction](https://mlsanity.com/paper/2205.04204/) (2022)
- [Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation](https://mlsanity.com/paper/1910.02702/) (2019)
- [Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement](https://mlsanity.com/paper/2107.13595/) (2021)
- [Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network](https://mlsanity.com/paper/2206.06341/) (2022)
- [Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising](https://mlsanity.com/paper/2302.13546/) (2023)
- [Quasi-supervised Learning for Super-resolution PET](https://mlsanity.com/paper/2209.01325/) (2022)
- [Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?](https://mlsanity.com/paper/2202.10998/) (2022)

## Built on by (most cited first, 1 of 1)

- [MlPET: A Localized Neural Network Approach for Probabilistic Post-Reconstruction PET Image Analysis Using Informed Priors](https://mlsanity.com/paper/2601.18021/) (2026)

## 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.