Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
author Zijdenbos, A.P
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Deep learning model on large uniform healthy MRI dataset achieves brain age MAE of 4.06 years (hold-out) and 4.21 years (independent set), with frontal lobe prominence and age gaps linked to neuropsychological scores.
A public GPU workflow for non-Fourier SENSE MRI reconstruction with sensitivity and off-resonance mapping enables fast, accurate imaging from challenging spiral trajectories.
citing papers explorer
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Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
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Estimating brain age based on a healthy population with deep learning and structural MRI
Deep learning model on large uniform healthy MRI dataset achieves brain age MAE of 4.06 years (hold-out) and 4.21 years (independent set), with frontal lobe prominence and age gaps linked to neuropsychological scores.
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A GPU-enhanced workflow for non-Fourier SENSE reconstruction
A public GPU workflow for non-Fourier SENSE MRI reconstruction with sensitivity and off-resonance mapping enables fast, accurate imaging from challenging spiral trajectories.