Analytic quantification of bias and variance of coil sensitivity profile estimators for improved image reconstruction in MRI.
Analytic quantification of bias and variance of coil sensitivity profile estimators for improved image reconstruction in MRI.
复制标题
对线圈灵敏度分布估计器的偏差和方差进行分析量化，以改进 MRI 中的图像重建。
DOI：
10.1007/978-3-319-24571-3_82
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Warfield,SimonK
中科院分区：
文献类型：
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作者：
Stamm,Aymeric;Singh,Jolene;Afacan,Onur;Warfield,SimonK
磁共振(MR)成像提供了一种独特的体内非侵入性可视化人脑组织的能力，在过去的几十年里极大地改善了患者的护理。然而，仍然存在明显的伪影，例如由于使用接收线圈阵列(RC)来测量MR信号而导致的强度不均匀，或者由于加速成像策略而导致的噪声放大。对于视觉检查和定量分析来说，减少这些伪影是至关重要的。解决这个问题的基础是了解RCS的线圈灵敏度分布(CSP)，它描述了测量的复杂信号如何随着到RC的距离而衰减。现有的CSP估计方法存在一些限制：(I)它们主要关注CSP的大小，而已知的MR图像重建问题的解决方案涉及复杂的CSP，以及(Ii)它们只提供CSP的点估计，这使得优化CSP估计的参数和采集协议的任务变得困难。在本文中，我们提出了一种新的估计复值CSP的统计框架。我们定义了一种CSP估计器，它使用空间平滑和附加体线圈数据进行相位归一化。其主要贡献是提供关于CSP估计器的统计分布的详细信息，从而自动确定确保最小偏差的最佳平滑程度，并为最佳捕获策略提供指导。
Magnetic resonance (MR) imaging provides a unique in-vivo capability of visualizing tissue in the human brain non-invasively, which has tremendously improved patient care over the past decades. However, there are still prominent artifacts, such as intensity inhomogeneities due to the use of an array of receiving coils (RC) to measure the MR signal or noise amplification due to accelerated imaging strategies. It is critical to mitigate these artifacts for both visual inspection and quantitative analysis. The cornerstone to address this issue pertains to the knowledge of coil sensitivity profiles (CSP) of the RCs, which describe how the measured complex signal decays with the distance to the RC.Existing methods for CSP estimation share a number of limitations: (i) they primarily focus on CSP magnitude, while it is known that the solution to the MR image reconstruction problem involves complex CSPs and (ii) they only provide point estimates of the CSPs, which makes the task of optimizing the parameters and acquisition protocol for their estimation difficult. In this paper, we propose a novel statistical framework for estimating complex-valued CSPs. We define a CSP estimator that uses spatial smoothing and additional body coil data for phase normalization. The main contribution is to provide detailed information on the statistical distribution of the CSP estimator, which yields automatic determination of the optimal degree of smoothing for ensuring minimal bias and provides guidelines to the optimal acquisition strategy.