ArticleIEEE transactions on radiation and plasma medical sciences2024
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.
Article in IEEE transactions on radiation and plasma medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- First-in-Human Imaging Results of PHAROS: A Versatile High-Resolution TOF/DOI PET Scanner for Brain, Breast, and Extremity Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising.IEEE transactions on medical imaging · 2026Article
- Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.European journal of nuclear medicine and molecular imaging · 2026Article
- A Cross-modality Transformer Network for MR-guided Low-dose Tau PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
- Multimodal Machine Learning Integrating N-13 Ammonia PET and Clinical Variables Predicts Major Adverse Cardiac Events.Journal of imaging informatics in medicine · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- A digital twin of the Biograph Vision Quadra long axial field of view PET/CT: Monte Carlo simulation and image reconstruction framework.EJNMMI physics · 2025Article
- Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.European journal of nuclear medicine and molecular imaging · 2024Article
- Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.Scientific reports · 2024Article
- Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.EJNMMI physics · 2024Article
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.
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Registered trials
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