  Supervised deep learning techniques can be used to generate synthetic 7T MRIs
from 3T MRI inputs. This image enhancement process leverages the advantages of
ultra-high-field MRI to improve the signal-to-noise and contrast-to-noise
ratios of 3T acquisitions. In this paper, we introduce multiple novel 7T
synthesization algorithms based on custom-designed variants of the V-Net
convolutional neural network. We demonstrate that the V-Net based model has
superior performance in enhancing both single-site and multi-site MRI datasets
compared to the existing benchmark model. When trained on 3T-7T MRI pairs from
8 subjects with mild Traumatic Brain Injury (TBI), our model achieves
state-of-the-art 7T synthesization performance. Compared to previous works,
synthetic 7T images generated from our pipeline also display superior
enhancement of pathological tissue. Additionally, we implement and test a data
augmentation scheme for training models that are robust to variations in the
input distribution. This allows synthetic 7T models to accommodate
intra-scanner and inter-scanner variability in multisite datasets. On a
harmonized dataset consisting of 18 3T-7T MRI pairs from two institutions,
including both healthy subjects and those with mild TBI, our model maintains
its performance and can generalize to 3T MRI inputs with lower resolution. Our
findings demonstrate the promise of V-Net based models for MRI enhancement and
offer a preliminary probe into improving the generalizability of synthetic 7T
models with data augmentation.
