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多组非双射曲线磁场编码的 MRI 数据重建。

Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields.

机构信息

Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.

出版信息

Magn Reson Med. 2012 Oct;68(4):1145-56. doi: 10.1002/mrm.24115. Epub 2012 Jan 13.

DOI:10.1002/mrm.24115
PMID:22246786
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3736833/
Abstract

Parallel imaging technique using localized gradients (PatLoc) uses the combination of surface gradient coils generating nonbijective curvilinear magnetic fields for spatial encoding. PatLoc imaging using one pair of multipolar spatial encoding magnetic fields (SEMs) has two major caveats: (1) The direct inversion of the encoding matrix requires exact determination of multiple locations which are ambiguously encoded by the SEMs. (2) Reconstructed images have a prominent loss of spatial resolution at the center of field-of-view using a symmetric coil array for signal detection. This study shows that a PatLoc system actually has a higher degree of freedom in spatial encoding to mitigate the two challenges mentioned above. Specifically, a PatLoc system can generate not only multipolar but also linear SEMs, which can be used to reduce the loss of spatial resolution at the field-of-view center. Here, we present an efficient and generalized image reconstruction method for PatLoc imaging using multiple SEMs without explicitly identifying the locations where SEM encoding is not unique. Reconstructions using simulations and empirical experimental data are compared with those using conventional linear gradients to demonstrate that the general combination of SEMs can improve image reconstructions.

摘要

使用局部梯度的并行成像技术(PatLoc)利用产生非一一对应曲线磁场的表面梯度线圈组合进行空间编码。使用一对多极空间编码磁场(SEMs)的 PatLoc 成像有两个主要的限制:(1) 编码矩阵的直接反演需要精确确定多个位置,这些位置由 SEMs 模糊编码。(2) 使用对称线圈阵列进行信号检测时,重建图像在视场中心的空间分辨率明显下降。本研究表明,PatLoc 系统实际上在空间编码方面具有更高的自由度,可以减轻上述两个挑战。具体来说,PatLoc 系统不仅可以产生多极 SEM,还可以产生线性 SEM,这可以用来减少视场中心的空间分辨率损失。在这里,我们提出了一种使用多个 SEM 进行 PatLoc 成像的高效、通用的图像重建方法,而无需明确识别 SEM 编码不唯一的位置。使用模拟和经验实验数据进行的重建与使用传统线性梯度的重建进行了比较,以证明 SEM 的通用组合可以改善图像重建。

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5
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