Abstract
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.
Abstract (translated)
监督深度学习技术可用于从3T MRI输入生成合成7T MRI。这种图像增强过程利用了超高清MRI的优势来提高3T扫描的信号与噪声比和对比与噪声比。在本文中，我们基于自定义设计的V-Net卷积神经网络引入了多个新的7T合成算法。我们证明了基于V-Net的模型在增强单站点和多站点MRI数据集方面比现有基准模型具有卓越性能。当用8个受轻度创伤性脑损伤（TBI）的患者进行训练时，我们的模型在增强7T合成性能方面达到了最先进的水平。与之前的工作相比，我们通过我们的管道生成的合成7T图像还突出了病理组织增强。此外，我们还实现并测试了一个数据增强方案，用于训练对输入分布的变化具有鲁棒性的模型。这使得合成7T模型能够适应多站点数据集中的内部和间歇性变化。在一个由两个机构共18个3T-7T MRI对组成的和谐数据集中，我们的模型保持其性能，并可以降低分辨率后的3T MRI输入。我们的研究结果证明了V-Net基于模型的MRI增强前景，并为提高合成7T模型的泛化性提供了初步试探。
URL
https://arxiv.org/abs/2403.08979
