ArticleIEEE transactions on radiation and plasma medical sciences2022
PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients.
Article in IEEE transactions on radiation and plasma medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).EJNMMI physics · 2026Article
- Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data.Medical image analysis · 2026Article
- Scatter correction for self-collimating SPECT using a 3D U-Net framework.American journal of nuclear medicine and molecular imaging · 2026Article
- Trans pixelate substitution scheme for denoising computed tomography images towards high diagnosis accuracy.Scientific reports · 2025Article
- Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision.Medical image analysis · 2025Article
- Deep Learning Denoising Improves and Homogenizes Patient [Diagnostics (Basel, Switzerland) · 2023Article
- A personalized deep learning denoising strategy for low-count PET images.Physics in medicine and biology · 2022Article
- AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.Tomography (Ann Arbor, Mich.) · 2022Article
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Authors and funding
13 authors.
Funding
Abstract
The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (
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