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.

用监督深度学习技术可以从 3T MRI 输入生成合成的 7T MRI。本文介绍了多种基于自定义变体的 V-Net 卷积神经网络的 7T 合成算法。我们证明基于 V-Net 模型在增强单点和多点 MRI 数据集方面的性能优于现有基准模型。我们的模型在训练了 8 个轻微创伤性脑损伤（TBI）患者的 3T-7T MRI 对后，在合成 7T 方面实现了最先进的性能。与以前的研究相比，我们的管道生成的合成 7T 图像还显示了对病理组织的优越增强效果。此外，我们还实施和测试了一种对输入分布变化具有鲁棒性的模型训练数据增强方案。这使合成的 7T 模型能够适应多中心数据集中的扫描仪内部和跨扫描仪的变异性。在包括健康受试者和轻微 TBI 患者的两个机构的 18 对 3T-7T MRI 数据集上，我们的模型保持其性能，并能在分辨率较低的 3T MRI 输入上泛化。我们的发现证明了基于 V-Net 模型在 MRI 增强方面的潜力，并为增强合成 7T 模型的泛化能力进行了初步探索。