Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior

Marko Panić; Jan Aelterman; Vladimir Crnojević; Aleksandra Pižurica

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Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior

机译：马尔可夫随机场先验在磁共振成像中的稀疏恢复

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摘要

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.

机译：磁共振成像（CS-MRI）压缩感知的最新研究强调了在采集或重建阶段对结构化稀疏度进行建模的重要性。典型图像的子带系数显示某些结构模式，可以按照固定组（如小波树）或通过统计方式（某些配置比其他配置更有可能）来查看。小波树模型已经显示出在从部分数据中进行MRI恢复方面的出色性能。然而，尽管已经表明了这种模型的潜力，但是在CS-MRI中很少关注子带数据的统计空间聚类建模。在本文中，我们提出了一种实用的CS-MRI重建算法，该算法利用马尔可夫随机场先验模型对子带系数进行空间聚类，并提出了一种基于近端分裂的有效优化方法。结果表明，与标准CS-MRI方法和最近的相关方法相比，重建性能都有改善。

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来源
《IEEE Transactions on Medical Imaging》 |2017年第10期|2104-2115|共12页
作者
Marko Panić; Jan Aelterman; Vladimir Crnojević; Aleksandra Pižurica;
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作者单位

BioSense Institute, University of Novi Sad, Novi Sad, Serbia;

Department Telecommunications and Information Processing, Ghent University, Ghent, Belgium;

BioSense Institute, University of Novi Sad, Novi Sad, Serbia;

Department Telecommunications and Information Processing, Ghent University, Ghent, Belgium;

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正文语种 eng
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关键词
Magnetic resonance imaging; Hidden Markov models; Transforms; Matching pursuit algorithms; Image reconstruction; Markov random fields; Data models;

机译：磁共振成像;隐马尔可夫模型;变换;匹配追踪算法;图像重建;马尔可夫随机场;数据模型;

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8. Sparse Signal Recovery Using Markov Random Fields [R] . Cevher, V., Hegde, C., Duarte, M. F., 2009

机译：利用马尔可夫随机场进行稀疏信号恢复

Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior

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