Learning to Synthesize 7 T MRI from 3 T MRI with Few Data by Deformable Augmentation.

High-quality magnetic resonance imaging (MRI), which is generally acquired by ultra-high field (7-Tesla, 7 T) MRI scanners, may lead to improved performance for brain disease diagnosis, such as Alzheimer’s disease (AD). However, 7 T MRI has not been widely used due to higher cost and longer scanning time. To overcome this, we proposed to utilize the generative adversarial networks (GAN)-based techniques to synthesize the 7 T scans from 3 T scans, for which, the most challenge is that we do not have enough data to learn a reliable mapping from 3 T to 7 T. To address this, we further proposed the Unlimited Data Augmentation (UDA) strategy to increase the learning samples via the deformable registration, which can produce enough paired 3 T and 7 T MR images to learning this mapping. Based on this mapping, we synthesize a 7 T MR scan for each subject in Alzheimer’s Disease Neuroimaging Initiative (ADNI), and conduct some experiments to evaluate their effect in two tasks of AD diagnosis, including AD identification and mild cognitive impairment (MCI) conversion prediction. Experimental results demonstrate that our UDA strategy is effective to learn a reliable mapping to high-quality MR images, and the synthetic 7 T scans are possible to increase the performance of AD diagnosis.