ArticleRadiology. Artificial intelligence2021
CT-less Direct Correction of Attenuation and Scatter in the Image Space Using Deep Learning for Whole-Body FDG PET: Potential Benefits and Pitfalls.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Large axial field of view PET/CT - Not just a longer PET gantry.Zeitschrift fur medizinische Physik · 2026Review
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Article
- Multidimensional lung imaging: integrated preclinical platforms enabling the identification of translational biomarkers for pulmonary research.Respiratory research · 2026Review
- Large scale comparison of maximum likelihood scatter scaling and tail-fitted scatter scaling in LAFOV PET/CT.EJNMMI physics · 2025Article
- CT-free attenuation and Monte-Carlo based scatter correction-guided quantitativeEuropean journal of nuclear medicine and molecular imaging · 2025Article
- A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.Radiological physics and technology · 2025Article
- AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.Polish journal of radiology · 2025Article
- CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
- Context-Aware Transformer GAN for Direct Generation of Attenuation and Scatter Corrected PET Data.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Differential privacy preserved federated transfer learning for multi-institutionalEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Prior information-guided reconstruction network for positron emission tomography images.Quantitative imaging in medicine and surgery · 2023Article
- Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance.Clinical nuclear medicine · 2023Article
- Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.Medical physics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo demonstrate the feasibility of CT-less attenuation and scatter correction (ASC) in the image space using deep learning for whole-body PET, with a focus on the potential benefits and pitfalls. MATERIALS AND
methodsIn this retrospective study, 110 whole-body fluorodeoxyglucose (FDG) PET/CT studies acquired in 107 patients (mean age ± standard deviation, 58 years ± 18; age range, 11-92 years; 72 females) from February 2016 through January 2018 were randomly collected. A total of 37.3% (41 of 110) of the studies showed metastases, with diverse FDG PET findings throughout the whole body. A U-Net-based network was developed for directly transforming noncorrected PET (PET
resultsThe normalized root mean square error (0.21 ± 0.05 [mean SUV ± standard deviation]), mean peak signal-to-noise ratio (36.3 ± 3.0), mean structural similarity index (0.98 ± 0.01), and voxelwise correlation (97.62%) of PET
conclusionDeep learning-based direct ASC at whole-body PET is feasible and potentially can be used to overcome the current limitations of CT-based approaches, benefiting patients who are sensitive to radiation from CT.
Identifiers
What OpenQuestion holds
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.