Bayesian parallel Imaging with edge-preserving priors
Bayesian parallel Imaging with edge-preserving priors
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DOI：
10.1002/mrm.21012
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发表时间：
2007-01-01
影响因子：
3.3
通讯作者：
Weiner, Michael
中科院分区：
文献类型：
--
作者：
Raj, Ashish;Singh, Gurmeet;Weiner, Michael
现有的平行MRI方法受到基本权衡的限制，因为抑制噪声会引入混叠伪像。具有适当选择的图像的贝叶斯方法提供了有希望的替代方案；但是，先前使用空间先验的方法假设强度在整个图像上平稳变化，从而导致边缘模糊。在这里，我们介绍了一个边缘保存的先验（EPP），该先验是假设强度平滑的，并提出了一种有效计算其贝叶斯估计值的新方法。估计任务被提出为优化问题，需要在具有数千个维度的空间中最小化非凸目标函数。结果，传统的连续最小化方法无法应用。此优化任务与在过去几年中开发了离散优化方法的计算机视觉领域的某些问题密切相关。我们适应基于图形的这些算法，以解决我们的优化问题。显示了在具有较高加速因子的挑战性条件下进行的大脑和躯干区域的几个平行成像实验的结果，并将其与常规灵敏度编码（Sense）方法的结果进行了比较。经验分析表明，与传统方法相比，该提出的方法在视觉上提高了整体质量。
Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a non-convex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.