Haute École de la Province de Liège

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.