摘要翻译：
在磁共振成像(MRI)领域，已经提出了一系列广泛的非线性重建算法，这些算法可以用于一般的傅立叶亚采样模式。然而，这些亚采样模式的设计通常是从重建规则和所考虑的解剖学中孤立考虑的。在本文中，我们提出了一个基于学习的框架来优化MRI子采样模式，针对特定的重建规则和解剖结构，同时考虑无噪声和有噪声的设置。我们的学习算法可以访问一组具有代表性的训练信号，并搜索对这组信号平均表现良好的采样模式。提出了一种新的无参数贪婪掩码选择方法，并证明了该方法对各种重构规则和性能指标都是有效的。此外，我们还通过统计学习理论对我们的框架提供了严格的证明，从而支持了我们的数值发现。
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英文标题：
《Learning-Based Compressive MRI》
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作者：
Baran G\"ozc\"u, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Il{\i}cak,
  Tolga \c{C}ukur, Jonathan Scarlett, Volkan Cevher
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最新提交年份：
2018
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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。
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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。
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英文摘要：
  In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method, and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.
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PDF链接：
https://arxiv.org/pdf/1805.01266