# POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation

arXiv: https://arxiv.org/abs/2401.14285 · HTML: https://mlsanity.com/paper/2401.14285/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation](https://mlsanity.com/paper/2010.01663/) (2020)
- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)
- [Unified Noise-aware Network for Low-count PET Denoising](https://mlsanity.com/paper/2304.14900/) (2023)
- [Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency](https://mlsanity.com/paper/2009.01782/) (2020)

## Built on by (most cited first, 3 of 3)

- [2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction](https://mlsanity.com/paper/2406.08374/) (2024)
- [Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation](https://mlsanity.com/paper/2405.12223/) (2024)
- [PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation](https://mlsanity.com/paper/2606.15323/) (2026)

## Abstract (arXiv)

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of μ-map generation, resulting in the production of high-quality μ-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.