PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
Not on the map. Why?
Abstract (arXiv)
Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses increasing radiation exposure. Lowering doses increases Poisson noise, which current denoising methods fail to handle, causing distortions and artifacts. We propose a Poisson Consistent U-Net (PC-UNet) model with a new Poisson Variance and Mean Consistency Loss (PVMC-Loss) that incorporates physical data to improve image fidelity. PVMC-Loss is statistically unbiased in variance and gradient adaptation, acting as a Generalized Method of Moments implementation, offering robustness to minor data mismatches. Tests on PET datasets show PC-UNet improves physical consistency and image fidelity, proving its ability to integrate physical information effectively.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 10 Oct 2025.
Builds on
9 of 27 references.
References · 9
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
- 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
- 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
- 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
- 2024CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentationcited by 12
- 2023Solving Low-Dose CT Reconstruction via GAN with Local Coherencecited by 5
- 2019A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learningcited by 4
Built on by
Not cited yet.