ArticleEuropean journal of nuclear medicine and molecular imaging2021
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it, 150 citations in OpenAlex.
- Mapping the research landscape of PET/CT in lymphoma: insights from a bibliometric analysis.Frontiers in oncology · 2025Pooled 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
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Artificial Intelligence Across the PET Reconstruction Pipeline: An Update.AJR. American journal of roentgenology · 2026Review
- Feasibility Study of Deep Learning-Driven Image Restoration for Fast, Low-Count [Diagnostics (Basel, Switzerland) · 2026Article
- Optimizing acquisition time and injected dose inBMC medical informatics and decision making · 2026Article
- Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.EJNMMI research · 2026Article
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.Journal of clinical medicine · 2026Review
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
- Multi-modal low-dose medical imaging through instruction-guided unified AI.Frontiers in medicine · 2026Article
- A prospective comparison of early versus lateScientific reports · 2025Article
- Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025Article
- UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.Journal of applied clinical medical physics · 2025Article
- Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.European journal of nuclear medicine and molecular imaging · 2025Article
- Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.Journal of magnetic resonance imaging : JMRI · 2025Article
- EnhancingCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
12 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
Abstract
purposeTendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthesis from fast/low-dose (LD) whole-body (WB) PET images using deep learning techniques.
methodsInstead of using synthetic LD scans, two separate clinical WB
resultsCGAN scored 4.92 and 3.88 (out of 5) (adequate to good) for brain and neck + trunk, respectively. The average SUV bias calculated over normal tissues was 3.39 ± 0.71% and - 3.83 ± 1.25% for CGAN and RNET, respectively. Bland-Altman analysis reported the lowest SUV bias (0.01%) and 95% confidence interval of - 0.36, + 0.47 for CGAN compared with the reference FD images for malignant lesions.
conclusionCycleGAN is able to synthesize clinical FD WB PET images from LD images with 1/8th of standard injected activity or acquisition time. The predicted FD images present almost similar performance in terms of lesion detectability, qualitative scores, and quantification bias and variance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.