Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. The super-resolution approach is potentially promising to improve MR image quality without any hardware upgrade. In this article, we propose an ensemble learning and deep learning framework for MR image super-resolution. In our study, we first enlarged low resolution images using five commonly used super-resolution algorithms and obtained differentially enlarged image datasets with complementary priors. Then, a generative adversarial network (GAN) is trained with each dataset to generate super-resolution MR images. Finally, another GAN is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images. According to our results, the ensemble learning results outperform any single GAN output component. Compared with some state-of-the-art deep learning-based super-resolution methods, our approach is advantageous in suppressing artifacts and keeping more image details.

中文翻译：

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具有集成学习和互补先验的 MRI 超分辨率

磁共振成像 (MRI) 是一种广泛使用的医学成像方式。然而，由于硬件、扫描时间和吞吐量的限制，获得高质量的 MR 图像在临床上通常具有挑战性。超分辨率方法有望在无需任何硬件升级的情况下提高 MR 图像质量。在本文中，我们提出了一种用于 MR 图像超分辨率的集成学习和深度学习框架。在我们的研究中，我们首先使用五种常用的超分辨率算法放大低分辨率图像，并获得具有互补先验的差异放大图像数据集。然后，使用每个数据集训练生成对抗网络 (GAN) 以生成超分辨率 MR 图像。最后，另一个 GAN 用于集成学习，将 GAN 的输出协同到最终的 MR 超分辨率图像中。根据我们的结果，集成学习结果优于任何单个 GAN 输出组件。与一些最先进的基于深度学习的超分辨率方法相比，我们的方法在抑制伪影和保留更多图像细节方面具有优势。