University of Liège

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发表出处待核验 Sep 30, 2026
Estimating SV2A PET-Derived Synaptic Density from Quantitative MRI: 3D U-Net Reaches Pearson Correlation of 0.8838 in Gray Matter
Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
University of Liège Haute École de la Province de Liège
Axel Ruymaekers（First author）
Synaptic Density Estimation from Quantitative MRI U-Net Generative Adversarial Networks Quantitative MRI
ORBi (University of Liège) Sep 22, 2026
How to Suspect Long Covid in General Practice: Two Clinical Cases, Two Interviews, and AI Assistance in a Belgian Dodécagroupe
This pedagogical experience report uses two recent clinical situations of women around 30 years old to show how a general practitioner can, by chronologically rereading the electronic medical record and combining it with recorded interviews conducted with patient consent, reassemble scattered fatigue, neurocognitive and language difficulties, respiratory and dysautonomic manifestations, sensory changes, and pain into a clinical configuration compatible with Long Covid, while using a language model only to extract clinical concepts from interview verbatims for medical validation and later comparison with the Human Phenotype Ontology (HPO), and while insisting on preserving differential diagnoses, documenting functional impact, and adapting activity to the patient's limits rather than increa
This pedagogical experience report uses two recent clinical situations of women around 30 years old to show how a general practitioner can, by chronologically rereading the electronic medical record and combining it with recorded interviews conducted with patient consent, reassemble scattered fatigue, neurocognitive and language difficulties, respiratory and dysautonomic manifestations, sensory changes, and pain into a clinical configuration compatible with Long Covid, while using a language model only to extract clinical concepts from interview verbatims for medical validation and later comparison with the Human Phenotype Ontology (HPO), and while insisting on preserving differential diagnoses, documenting functional impact, and adapting activity to the patient's limits rather than increa
University of Liège
Marc Jamoulle（First author, Corresponding author）
Long Covid Human Phenotype Ontology AI-assisted clinical concept extraction from interview verbatim

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Estimating SV2A PET-Derived Synaptic Density from Quantitative MRI: 3D U-Net Reaches Pearson Correlation of 0.8838 in Gray Matter

How to Suspect Long Covid in General Practice: Two Clinical Cases, Two Interviews, and AI Assistance in a Belgian Dodécagroupe