High-resolution (HR) magnetic resonance images (MRI) provide more detailed information for clinical application. However, HR MRI is less available because of the longer scan time and lower signal-to-noise ratio. Spatial resolution is one of the key parameters of MRI. The image post-processing technique super-resolution (SR) is an alternative approach to improve the spatial resolution of MR images. Inspired by advanced deep learning based SR methods, we propose an MRI SR model named progressive sub-band residual learning SR network (PSR-SRN). The proposed model contains two parallel progressive learning streams, where one stream learns on missed high-frequency residuals by sub-band residual learning unit (ISRL) and the other focuses on reconstructing refined MR image. These two streams complement each other and enable to learn complex mappings between “Low-” and “High-” resolution MR images. Besides, we introduce brain-like mechanisms (in-depth supervision and local feedback mechanism) and progressive sub-band learning strategy to emphasize variant textures of MRI. Compared with traditional and deep learning MRI SR methods, our PSR-SRN model shows superior performance.

中文翻译：

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渐进式子带残差学习网络，用于MR图像超分辨率

高分辨率（HR）磁共振图像（MRI）为临床应用提供了更详细的信息。但是，由于扫描时间较长且信噪比较低，因此HR MRI的可用性较差。空间分辨率是MRI的关键参数之一。图像后处理技术超分辨率（SR）是提高MR图像空间分辨率的一种替代方法。受基于深度学习的高级SR方法的启发，我们提出了一种MRI SR模型，称为渐进子带残差学习SR网络（PSR-SRN）。所提出的模型包含两个并行的渐进式学习流，其中一个流通过子带残差学习单元（ISRL）学习丢失的高频残差，而另一个流专注于重建精炼的MR图像。这两个流相互补充，使您能够了解“低”和“高分辨率” MR图像之间的复杂映射。此外，我们介绍了类似大脑的机制（深度监督和本地反馈机制）和渐进式子带学习策略，以强调MRI的不同纹理。与传统和深度学习MRI SR方法相比，我们的PSR-SRN模型显示出卓越的性能。