摘要翻译：
高分辨率磁共振成像(MRI)在许多临床应用中是理想的，然而，在分辨率、采集速度和噪声之间存在着一个折衷问题。MR图像的通面分辨率~（层厚）比面内分辨率差是很常见的。在这些MRI图像中，通面方向上的高频信息不能被获取，也不能通过插值进行解析。为了解决这一问题，人们发展了超分辨率方法来提高空间分辨率。作为一个不适定问题，现有的超分辨率方法依赖于外部/训练图谱的存在来学习从低分辨率~(LR)图像到高分辨率~(HR)图像的转换。由于几个原因，这种HR图谱通常不能用于MRI序列。本文提出了一种自超分辨率~(SSR)算法，该算法不使用任何外部地图集图像，但仅依赖于获取的LR图像仍能分辨HR图像。我们使用输入图像的模糊版本来创建一个最先进的超分辨率深度网络的训练数据。将训练好的网络应用于原始输入图像，对HR图像进行估计。我们的SSR结果表明，与竞争的SSR方法相比，通过平面分辨率有了显著的提高。
---
英文标题：
《Self Super-Resolution for Magnetic Resonance Images using Deep Networks》
---
作者：
Can Zhao, Aaron Carass, Blake E. Dewey, and Jerry L. Prince
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--

---
英文摘要：
  High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.
---
PDF链接：
https://arxiv.org/pdf/1802.09431