摘要翻译：
多采集磁共振成像(MRI)的一个主要限制是随着不同数据集数量的增加，扫描效率下降。稀疏恢复技术可以通过随机欠采样获取来缓解这一限制。频繁采样策略是为每次采集规定从公共采样密度中提取的不同随机模式。然而，原始随机模式往往在采集维度上包含间隙或簇，这反过来会降低重建质量或降低扫描效率。针对这一问题，提出了一种用于多采集MRI的统计隔离采样方法。该方法顺序生成多个模式，同时自适应地修改采样密度以最小化模式间的K空间重叠。结果，它改善了采集之间的不相干性，同时仍然在K空间的径向维上保持相似的采样密度。给出了相位循环平衡稳态自由进动和多回波T2加权成像的综合模拟和在体结果。分离采样在多次采集数据集的傅立叶重建和压缩感知重建中都显著提高了质量。
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英文标题：
《Statistically Segregated k-Space Sampling for Accelerating
  Multiple-Acquisition MRI》
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作者：
L Kerem Senel, Toygan Kilic, Alper Gungor, Emre Kopanoglu, H Emre
  Guven, Emine U Saritas, Aykut Koc, Tolga Cukur
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最新提交年份：
2017
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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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英文摘要：
  A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T$_2$-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.
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PDF链接：
https://arxiv.org/pdf/1710.00532