MRI super-resolution (SR) and denoising tasks are fundamental challenges in the field of deep learning, which have traditionally been treated as distinct tasks with separate paired training data. In this paper, we propose an innovative method that addresses both tasks simultaneously using a single deep learning model, eliminating the need for explicitly paired noisy and clean images during training. Our proposed model is primarily trained for SR, but also exhibits remarkable noise-cleaning capabilities in the super-resolved images. Instead of conventional approaches that introduce frequency-related operations into the generative process, our novel approach involves the use of a GAN model guided by a frequency-informed discriminator. To achieve this, we harness the power of the 3D Discrete Wavelet Transform (DWT) operation as a frequency constraint within the GAN framework for the SR task on magnetic resonance imaging (MRI) data. Specifically, our contributions include: 1) a 3D generator based on residual-in-residual connected blocks; 2) the integration of the 3D DWT with $1\times 1$ convolution into a DWT+conv unit within a 3D Unet for the discriminator; 3) the use of the trained model for high-quality image SR, accompanied by an intrinsic denoising process. We dub the model "Denoising Induced Super-resolution GAN (DISGAN)" due to its dual effects of SR image generation and simultaneous denoising. Departing from the traditional approach of training SR and denoising tasks as separate models, our proposed DISGAN is trained only on the SR task, but also achieves exceptional performance in denoising. The model is trained on 3D MRI data from dozens of subjects from the Human Connectome Project (HCP) and further evaluated on previously unseen MRI data from subjects with brain tumours and epilepsy to assess its denoising and SR performance.

翻译：MRI超分辨率（SR）与去噪任务是深度学习领域的基础性挑战，传统上这两类任务需使用各自独立的成对训练数据分别处理。本文提出一种创新方法，通过单一深度学习模型同时解决这两项任务，无需在训练过程中显式提供成对的噪声图像与干净图像。所提出的模型主要针对超分辨率任务进行训练，但在超分辨结果中展现出显著的噪声消除能力。与将频域相关操作引入生成过程的传统方法不同，我们的创新之处在于采用由频率信息驱动的判别器引导的GAN模型。为此，我们利用3D离散小波变换（DWT）操作作为频率约束，将其融入磁共振成像（MRI）数据超分辨率任务的GAN框架中。具体贡献包括：1）基于残差嵌套残差连接块的3D生成器；2）将3D DWT与$1\times 1$卷积整合为DWT+conv单元，嵌入判别器的3D Unet结构中；3）利用训练模型实现高质量图像超分辨率，并附带固有去噪过程。由于该模型兼具超分辨率图像生成与同步去噪的双重效果，我们将其命名为“去噪诱导超分辨率GAN（DISGAN）”。与将超分辨率与去噪任务分别训练不同，我们的DISGAN仅基于超分辨率任务进行训练，却能在去噪方面取得卓越性能。该模型利用人体连接组计划（HCP）中数十名受试者的3D MRI数据进行训练，并在包含脑肿瘤与癫痫数据的未见MRI数据上进一步评估其去噪与超分辨率性能。