TY  - CONF
AU  - Gicquel, Malo
AU  - Flood, Gabrielle
AU  - Zhao, Ruoyi
AU  - Wuestefeld, Anika
AU  - Spotorno, Nicola
AU  - Strandberg, Olof
AU  - Xiao, Yu
AU  - Åström, Kalle
AU  - Wisse, Laura E M
AU  - van Westen, Danielle
AU  - Berron, David
AU  - Hansson, Oskar
AU  - Vogel, Jacob W
TI  - AI Superresolution: Converting T1‐weighted MRI from 3T to 7T resolution toward enhanced imaging biomarkers for Alzheimer’s disease
JO  - Alzheimer's and dementia
VL  - 21
IS  - Suppl 8
SN  - 1552-5260
M1  - DZNE-2025-01506
SP  - e109817
PY  - 2025
AB  - High-resolution (7T) MRI facilitates in vivo imaging of fine anatomical structures selectively affected in Alzheimer's disease (AD), including medial temporal lobe subregions. However, 7T data is challenging to acquire and largely unavailable in clinical settings. Here, we use deep learning to synthesize 7T resolution T1-weighted MRI images from lower-resolution (3T) images.Paired 7T and 3T T1-weighted images were acquired from 178 participants (134 clinically unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T-resolution images from 3T images, we trained two models: a specialized U-Net, and a U-Net mixed with a generative adversarial network (U-Net-GAN) on 80
T2  - Alzheimer’s Association International Conference
CY  - 27 Jul 2025 - 31 Jul 2025, Toronto (Canada)
Y2  - 27 Jul 2025 - 31 Jul 2025
M2  - Toronto, Canada
KW  - Humans
KW  - Alzheimer Disease: diagnostic imaging
KW  - Magnetic Resonance Imaging: methods
KW  - Female
KW  - Male
KW  - Aged
KW  - Deep Learning
KW  - Neuroimaging: methods
KW  - Image Processing, Computer-Assisted: methods
KW  - Brain: diagnostic imaging
KW  - Sweden
LB  - PUB:(DE-HGF)1 ; PUB:(DE-HGF)16
C6  - pmid:41434097
C2  - pmc:PMC12725268
DO  - DOI:10.1002/alz70862_109817
UR  - https://pub.dzne.de/record/283099
ER  -