Converting T1-weighted MRI from 3T to 7T quality using deep learning

Malo Gicquel; Ruoyi Zhao; Anika Wuestefeld; Nicola Spotorno; Olof Strandberg; Kalle Åström; Yu Xiao; Laura EM Wisse; Danielle van Westen; Rik Ossenkoppele; Niklas Mattsson-Carlgren; David Berron; Oskar Hansson; Gabrielle Flood; Jacob Vogel

arXiv · 2507.13782

Converting T1-weighted MRI from 3T to 7T quality using deep learning

Malo Gicquel·Ruoyi Zhao·Anika Wuestefeld·Nicola Spotorno·Olof Strandberg·Kalle Åström·Yu Xiao·Laura EM Wisse·Danielle van Westen·Rik Ossenkoppele·Niklas Mattsson-Carlgren·David Berron·Oskar Hansson·Gabrielle Flood·Jacob Vogel

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Abstract

Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3T MRI, though at the cost of accessibility. We present an advanced deep learning model for synthesizing 7T brain MRI from 3T brain MRI. Paired 7T and 3T T1-weighted images were acquired from 172 participants (124 cognitively unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T MRI from 3T images, we trained two models: a specialized U-Net, and a U-Net integrated with a generative adversarial network (GAN U-Net). Our models outperformed two previous state-of-the-art 3T-to-7T models in image-based evaluation metrics. Four blinded MRI professionals judged our synthetic 7T images as comparable in detail to real 7T images, and superior in subjective visual quality to 7T images, due to the reduction of artifacts. Using both SynthSeg and NextBrain, automated segmentations of the synthetic 7T images were more similar to real 7T segmentations than automated segmentations from the 3T images that were used to synthesize the 7T images. Finally, synthetic 7T images showed similar performance to real 3T images in downstream prediction of cognitive status using MRI derivatives (n=3,168). In all, we show that synthetic T1-weighted brain images approaching 7T quality can be generated from 3T images, which may improve image quality and segmentation, without compromising performance in downstream tasks. Future directions, possible clinical use cases, and limitations are discussed.

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Malo Gicquel, Ruoyi Zhao, Anika Wuestefeld, Nicola Spotorno, Olof Strandberg, Kalle Åström, Yu Xiao, Laura EM Wisse, Danielle van Westen, Rik Ossenkoppele, Niklas Mattsson-Carlgren, David Berron, Oskar Hansson, Gabrielle Flood, Jacob Vogel. 2025-07-18. Converting T1-weighted MRI from 3T to 7T quality using deep learning. https://arxiv.org/abs/2507.13782

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