Learning based single image super resolution (SISR) task is well investigated in 2D images. However, SISR for 3D Magnetics Resonance Images (MRI) is more challenging compared to 2D, mainly due to the increased number of neural network parameters, the larger memory requirement and the limited amount of available training data. Current SISR methods for 3D volumetric images are based on Generative Adversarial Networks (GANs), especially Wasserstein GANs due to their training stability. Other common architectures in the 2D domain, e.g. transformer models, require large amounts of training data and are therefore not suitable for the limited 3D data. However, Wasserstein GANs can be problematic because they may not converge to a global optimum and thus produce blurry results. Here, we propose a new method for 3D SR based on the GAN framework. Specifically, we use instance noise to balance the GAN training. Furthermore, we use a relativistic GAN loss function and an updating feature extractor during the training process. We show that our method produces highly accurate results. We also show that we need very few training samples. In particular, we need less than 30 samples instead of thousands of training samples that are typically required in previous studies. Finally, we show improved out-of-sample results produced by our model.

翻译：基于学习的单图像超分辨率（SISR）任务在二维图像中已得到充分研究。然而，三维磁共振图像（MRI）的SISR相较于二维更具挑战性，这主要源于神经网络参数数量的增加、更大的内存需求以及可用训练数据的有限性。当前三维体素图像的SISR方法基于生成对抗网络（GAN），特别是因其训练稳定性而广泛采用的Wasserstein GAN。二维领域中的其他常见架构（例如Transformer模型）需要大量训练数据，因此不适用于数据受限的三维场景。然而，Wasserstein GAN存在潜在问题：可能无法收敛至全局最优解，从而产生模糊结果。本文基于GAN框架提出了一种新的三维超分辨率方法。具体而言，我们引入实例噪声以平衡GAN训练，并在训练过程中使用相对论GAN损失函数及动态更新的特征提取器。实验表明，我们的方法可生成高精度结果，且仅需极少量训练样本（少于30个样本），而此前研究通常需要数千个训练样本。最后，我们展示了模型在样本外数据上的改进效果。