ArticleFrontiers in medicine2022
PET image enhancement using artificial intelligence for better characterization of epilepsy lesions.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy.European journal of nuclear medicine and molecular imaging · 2024Guideline
- Depth-Dependent Performance of Residual Networks for Low-Count PET Image Restoration Using a Dedicated 3D-Printed Striatum Phantom.Bioengineering (Basel, Switzerland) · 2026Article
- Mirrored structural symmetry index (VMSSI): a novel approach for diagnosing MR-negative focal cortical dysplasia using structural MRI.Frontiers in neuroscience · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach.Insights into imaging · 2024Article
- Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy.Epilepsia open · 2023Article
- Characterisation of a novel [EJNMMI research · 2023Article
- Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: [ Methods: We created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [ Results: Compared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence. Conclusion: P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.
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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.