ArticleJournal of magnetic resonance imaging : JMRI2024
Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.
Article in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 2026Article
- Automated machine learning-based unenhanced radiomics for identification of HER2-low breast cancers using diffusion-weighted imaging.BMC cancer · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Synthetic O-(2-Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundPURPOSE: To generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI. STUDY TYPE: Retrospective. SUBJECTS: Patients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent FIELD STRENGTH/SEQUENCE: 3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, ASSESSMENT: Convolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland-Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5-point scale, with score ≥3 as high-quality. They assessed the lesions on a 5-point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET. STATISTICAL TESTS: The agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance.
resultsThe synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, -31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high-quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall.
conclusionThe proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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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.