摘要翻译：
在定量磁共振成像中，由于空间分辨率的限制，传统方法受到所谓的部分体积效应(PVE)的影响。PVE的结果是，包含多个组织的体素参数不能正确估计。磁共振指纹(MRF)也不例外。现有的解决PVE的方法既不具有可扩展性，也不具有准确性。我们提出将每个体素的多个组织的恢复表述为一个非凸约束的最小二乘最小化问题。为了解决这一问题，我们提出了一种内存高效、贪婪的近似投影梯度下降算法，称为GAP-MRF。该方法自适应地在MRF序列定义的指纹流形上发现感兴趣区域。我们推广我们的方法来补偿出现在模型中的相位误差，使用交替最小化方法。我们用PVE对合成数据进行了仿真，结果表明我们的算法优于现有的方法。我们的方法在EUROSPIN模型和活体数据集上得到了验证。
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英文标题：
《Greedy Approximate Projection for Magnetic Resonance Fingerprinting with
  Partial Volumes》
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作者：
Roberto Duarte, Audrey Repetti, Pedro A. G\'omez, Mike Davies and Yves
  Wiaux
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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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英文摘要：
  In quantitative Magnetic Resonance Imaging, traditional methods suffer from the so-called Partial Volume Effect (PVE) due to spatial resolution limitations. As a consequence of PVE, the parameters of the voxels containing more than one tissue are not correctly estimated. Magnetic Resonance Fingerprinting (MRF) is not an exception. The existing methods addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of multiple tissues per voxel as a nonconvex constrained least-squares minimisation problem. To solve this problem, we develop a memory efficient, greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our method adaptively finds the regions of interest on the manifold of fingerprints defined by the MRF sequence. We generalise our method to compensate for phase errors appearing in the model, using an alternating minimisation approach. We show, through simulations on synthetic data with PVE, that our algorithm outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.
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PDF链接：
https://arxiv.org/pdf/1807.06912