Iteratively reweighted L1-fitting for model-independent outlier removal and regularization in diffusion MRI
Iteratively reweighted L1-fitting for model-independent outlier removal and regularization in diffusion MRI
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DOI：
10.1109/isbi.2016.7493413
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
A. Tobisch;T. Stöcker;S. Groeschel;T. Schultz
中科院分区：
文献类型：
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作者：
A. Tobisch;T. Stöcker;S. Groeschel;T. Schultz
扩散磁共振成像受到图像采集过程中发生的对象运动的负面影响。诱发的数据伪影对微结构扩散度量的估计产生不利影响。最先进的异常值去除程序在模型拟合过程中检测并拒绝有缺陷的图像。然而，这些方法只为特定的扩散模型量身定做，排除不同数量的扩散加权图像可能对参数估计不利。因此，本工作提出了一种新的方法，该方法基于迭代加权的LL拟合，用于去除与模型无关的孤立点，并通过在连续的肖特基上对信号进行建模来重建有缺陷的图像。我们在模拟数据和临床活体人脑扫描上验证了所提出的方法，并展示了其对峰度和节点模型所确定的扩散参数的影响。
Diffusion magnetic resonance imaging is negatively affected by subject motion occurring during the image acquisition. The induced data artifacts adversely influence the estimation of microstructural diffusion measures. State-of-the-art procedures for outlier removal detect and reject defective images during model fitting. These methods, however, are tailored only for specific diffusion models and excluding a varying number of diffusion-weighted images might be disadvantageous for the parameter estimation. Therefore, this work proposes a novel method based on an iteratively reweighted Ll-Fitting for model-independent outlier removal with subsequent reconstruction of faulty images by modeling the signal in the continuous SHORE basis. We validate the proposed method on simulation data and clinical in vivo human brain scans and demonstrate its effect on diffusion parameters determined by the kurtosis and NODDI model.